Computer-assisted tumor response assessment and evaluation of the vascular tumor burden

ABSTRACT

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images can include receiving cross-sectional images of digital medical image data and identifying target lesions within the cross-sectional images. For each of the target lesions, a target lesion type and anatomical location is identified, a segmenting tool is activated for segmenting the target lesions into regions of interest, lesion metrics are automatically extracted from the regions of interest according to tumor response criteria, and conformity of target lesion identification is monitored using rules associated with the tumor response criteria, prompting a user to address any nonconforming target lesion. The method also includes receiving a presence/absence of metastases, determining changes in lesions metrics, and deriving an objective tumor response based on the tumor response criteria.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/797,391, filed Oct. 30, 2017, which is a continuation of U.S. patentapplication Ser. No. 15/407,662, filed Jan. 17, 2017, which is acontinuation of International Application No. PCT/US2016/030823, filedMay 4, 2016, which claims the benefit of U.S. Provisional PatentApplication No. 62/156,836, filed May 4, 2015, the disclosures of whichare incorporated herein by reference in their entirety.

BACKGROUND Technical Field

The present invention relates generally to the collection, evaluation,and transformation of digital image files.

Background and Relevant Art

Advances in computing technology have resulted in a concomitant advancein medical device technologies, including within the field of diagnosticmedicine. Particularly, the past century has demonstrated significantadvances in medical imaging devices. Such advances have been hallmarkedby the advent of radiologic devices such as computed tomography (CT;also known as x-ray computed tomography (x-ray CT) and computerizedaxial tomography scan (CT scan)), magnetic resonance imaging, and otherradiologic devices that allow for the non-invasive viewing andexploration of internal structures of the body. These medical imagingdevices allow physicians and clinicians to better document, treat, andunderstand pathologies, including cancer.

A variety of tumor response criteria have been developed to predictand/or evaluate the effects of anti-cancer therapies. However, there areissues with the known tumor response criteria being inconsistent,difficult to implement, narrowly tailored, expensive, and not reliablyreproducible across multiple different institutions and readers.Further, the effects of some newer anti-cancer treatment therapies aredifficult to determine with the current tumor response criteria.

Accordingly, there are a number of disadvantages in the art of datacollection and management that can be addressed.

SUMMARY OF INVENTION Technical Problem

There is a need for a method or tumor imaging biomarker for assessingtumor response to anti-cancer therapies that could provide astraightforward quantitative metric or set of quantitative metrics thatis directly related to the main effect of newer anti-cancer therapies.This need is particularly exacerbated when considering the dearth ofmethods or tumor imaging biomarkers available for accurately assessingtumor response to anti-cancer therapies that cause tumordevascularization, including anti-angiogenic therapies. Current tumorimaging biomarkers and tumor response criteria fail to directly quantifychanges in the vascular tumor burden on cross-sectional images toeffectively monitor response to anti-cancer therapies, includinganti-angiogenic therapy, and to predict long-term response toanti-angiogenic therapy.

Further, the implementation of available tumor response criteria suffersfrom user-induced error, whether by miscalculations of tumor metrics,measurement errors, data transfer errors, mathematical errors, or byselecting target lesions that do not meet the standards of a given tumorresponse criteria. Additionally, there is a considerable amount ofintra- and inter-observer variability in determining an objective tumorresponse with conventional methods. Each of the foregoing may cause anincorrect classification of tumor response, which could negativelyimpact patient care, quality of life, and survival.

Solution to Problem

Implementations of the present invention comprise computer-implementedmethods and systems and computer programmable products configured todetermine and evaluate an objective tumor response to an anti-cancertherapy using cross-sectional images. In some embodiments, this caninclude receiving cross-sectional images of digital medical image dataand identifying target lesions within the cross-sectional images. Foreach of the target lesions, a target lesion type and anatomical locationis identified, a segmenting tool is activated for segmenting the targetlesions into regions of interest, lesion metrics are automaticallyextracted from the regions of interest according to tumor responsecriteria, and conformity of target lesion identification is monitoredusing rules associated with the tumor response criteria, prompting auser to address any nonconforming target lesion. The method alsoincludes receiving a presence/absence of metastases, determining changesin lesions metrics, and deriving an objective tumor response based onthe tumor response criteria.

Embodiments of the foregoing may be implemented in a computer assistedmanner for assessing and post-processing digital radiologiccross-sectional images of tumors to derive one or more tumor metrics,including total tumor burden and vascular tumor burden and may furtherderive objective tumor response via one or more tumor response criteria.In some embodiments, a user interacts with a computer interfaceresponsive to user selections and which updates a plurality of tumormetrics in response to user input and which may synthesize and display areport to the user summarizing results, including a vascular tumorburden and an objective tumor response.

Embodiments of the present disclosure provide a technical solution tothe aforementioned technical problems associated with reliably anddirectly quantifying changes in the vascular tumor burden usingcross-sectional images, with effectively monitoring responses toanti-cancer therapies whose main effect is to cause devascularization oftumors, including anti-angiogenic therapy, and with predicting long-termresponse to anti-angiogenic therapy. Further, embodiments of the presentdisclosure provide a technical solution to the aforementioned technicalproblem of incorrectly classifying tumor response based on user errorand may also act to reduce the amount of intra- and inter-observervariability in determining an objective tumor response.

Additional features and advantages of exemplary implementations of theinvention will be set forth in the description which follows, and inpart will be obvious from the description, or may be learned by thepractice of such exemplary implementations. The features and advantagesof such implementations may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. These and other features will become more fully apparent fromthe following description and appended claims, or may be learned by thepractice of such exemplary implementations as set forth hereinafter.

BRIEF DESCRIPTION OF DRAWINGS

In order to describe the manner in which the above recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by references to specific embodiments thereof, which areillustrated in the appended drawings. Furthermore, multiple instances ofan element may each include separate letters appended to the elementnumber. For example two instances of a particular element “200” may belabeled as “200 a” and “200 b”. In that case, the element label may beused without an appended letter (e.g., “200”) to generally refer toevery instance of the element, while the element label will include anappended letter (e.g., “200 a”) to refer to a specific instance of theelement. Understanding that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 is a schematic representation of a system for determining anobjective tumor response to an anti-cancer therapy using one or morecross-sectional images according to one or more embodiments of thepresent disclosure;

FIG. 2 is a schematic representation of a computer interface comprisinga customizable summary image according to one or more embodiments of thepresent disclosure;

FIG. 3 is a schematic representation of a computer interface comprisinga customizable summary image according to one or more embodiments of thepresent disclosure;

FIG. 4 depicts an implementation of the computer interface of FIG. 2according to one or more embodiments of the present disclosure;

FIG. 5 depicts an implementation of the computer interface of FIG. 3according to one or more embodiments of the present disclosure;

FIG. 6 illustrates a computer-implemented method of determining anobjective tumor response to an anti-cancer therapy throughuser-interaction with a computer interface provided in accordance withone or more embodiments of the present disclosure;

FIG. 7 illustrates a computer-implemented method of determining anobjective tumor response to an anti-cancer therapy throughuser-interaction with a computer interface provided in accordance withone or more embodiments of the present disclosure;

FIG. 8 illustrates a computer-implemented method of determining anobjective tumor response to an anti-cancer therapy throughuser-interaction with a computer interface provided in accordance withone or more embodiments of the present disclosure;

FIG. 9 illustrates a user-implemented method of interacting with acomputer interface to determine an objective tumor response to ananti-cancer therapy according to one or more embodiments of the presentdisclosure; and

FIG. 10 illustrates a user-implemented method of interacting with acomputer interface to determine an objective tumor response to ananti-cancer therapy according to one or more embodiments of the presentdisclosure.

FIG. 11 illustrates the results from a large clinical validation study,expressing how the VTB Criteria better predicts tumor response toanti-angiogenic therapy than other currently used imaging criteria.

DETAILED DESCRIPTION

While the detailed description is separated into sections, the sectionheaders and contents within each section are not intended to beself-contained descriptions and embodiments. Rather, the contents ofeach section within the detailed description are intended to be read andunderstood as a collective whole where elements of one section maypertain to and/or inform other sections. Accordingly, embodimentsspecifically disclosed within one section may also relate to and/orserve as additional and/or alternative embodiments in another sectionhaving the same and/or similar systems, modules, devices, methods,and/or terminology.

The embodiments disclosed herein will now be described by reference tosome more detailed embodiments, with occasional reference to anyapplicable accompanying drawings. These embodiments may, however, beembodied in different forms and should not be construed as limited tothe embodiments set forth herein. Rather, these embodiments are providedso that this disclosure will be thorough and complete, and will fullyconvey the scope of the embodiments to those skilled in the art.

Overview of Imaging Evaluation of Tumor Response and Use of TumorResponse Criteria

Imaging evaluation of tumor response to therapy is important forclinical trials and new drug development and routinely used for managingpatients with advanced malignancies. Radiologic cross-sectional imagingis frequently used in oncologic clinical trials and in routine patientmanagement regimens as a means of noninvasively evaluating response totherapy.

Traditional methods for assessing tumor response are based onmeasurements of target lesion length on radiologic cross-sectionalimages. The most commonly used tumor response assessment criterion isResponse Evaluation Criteria In Solid Tumors (RECIST). RECIST 1.0 wasoriginally developed in the year 2000 to evaluate solid tumor responseto cytotoxic chemotherapy but was simplified and improved in the year2009, resulting in RECIST 1.1. The improved RECIST 1.1 tumor responsecriterion serves as a longitudinal tumor response assessment tool thatallows the treating oncologic provider to assess disease severity overtime and to make better-informed anti-cancer treatment recommendations.

In order to perform RECIST 1.1, a user must strictly adhere to a numberof rules and steps, some of which guide target lesion selection,measurement, and assessment. RECIST 1.1 also provides a number ofmathematical, data transfer, and recording steps. This criterionassesses the tumor burden at two or more time points, and the finalclassification of tumor response to therapy is based on changes in thetumor burden, response of non-target lesions to therapy, and thepresence or absence of new metastases. An error in any step may lead toan incorrect classification of tumor response to therapy, which couldnegatively impact patient care, quality of life, and survival. Commonerrors include selecting target lesions that do not meet RECIST 1.1standards, measurement errors, data transfer errors, mathematicalerrors, and tumor response classification errors.

In the last decade, a number of new anti-cancer therapies have beendeveloped, including new targeted agents, immunotherapies, ablativetherapies, embolization therapies, and radiotherapies. While themechanism of action for each of these therapies may be unique, there areseveral commonalities between them. The overwhelming majority of tumorsrequire a vascular supply to remain viable or to grow. Many newertherapies disrupt tumor vascularity, either at macro- or micro-vessellevel, and directly or indirectly cause tumor devascularization.Collectively, these therapies can be considered anti-angiogenic (AAG).AAG therapies are used to treat metastatic disease emanating from allmajor tumors, including: lung, breast, prostate, colon, renal cellcarcinoma (RCC), hepatocellular carcinoma (HCC), melanoma, and manyothers.

Many effective anti-angiogenic therapies do not cause significantdecreases to tumor length when viewed by radiologic cross-sectionalimages, and RECIST 1.1, thereby, underestimates tumor response to AAGtherapy. A successful AAG therapy results in tumor devascularization,which can lead to moderate tumor shrinkage and/or marked changes invascularity as reflected by a decrease in tumor attenuation or visiblesigns of necrosis on contrast-enhanced CT images.

The ideal tumor imaging biomarker for patients with advanced cancertreated by AAG therapy could be used both as a longitudinal tumorresponse assessment tool and used to predict response to a AAG therapyafter one cycle of therapy. The ideal tumor imaging biomarker could be astraightforward quantitative metric or set of metrics that is or aredirectly related to the main effect of AAG therapy, namely tumordevascularization. Further, such an ideal tumor response criterion couldbe easy to use, widely available, widely applicable, amenable to highthroughput, inexpensive, and highly reproducible across multipledifferent institutions and readers.

However, the currently available tumor response criteria do not meet theforegoing characteristics of an ideal tumor response criterion. Allcurrent genetic and serum biomarkers have insufficient accuracy forclinical use, and none have been externally validated. A variety ofimaging biomarkers are under development, including positron emissiontomography fused with computed tomography images (PET CT), computedtomography perfusion (CTP) imaging, advanced magnetic resonance imaging(MRI) techniques, and post-processing techniques for routinecontrast-enhanced computed tomography (CT).

Evaluation of glucose metabolism by fluorodeoxyglucose (FDG) PET CT doesnot predict response to AAG. Newer PET radiotracers that evaluatespecific angiogenesis pathways have failed in clinical trials to predictoutcomes and to serve as a useful biomarker. CT perfusion is technicallychallenging, is semi-objective, is performed differently by eachmanufacturer, is not reproducible on different scanners or at differentinstitutions, is not widely available, requires large amounts ofprocessing time (not conducive to clinical workflow), and is only ableto evaluate large tumors in a single body region (not the entire body inpatients with diffuse metastatic disease). Advanced MRI techniques arenot reproducible between different scanners or institutions and cannotbe used to evaluate small lung metastases.

On the other hand, CT imaging is widely available, amenable to highthroughput, reliable, inexpensive, and reproducible across multiplecenters, making routine contrast-enhanced or unenhanced CT images themost promising imaging technique to serve as a foundation for generatingsurrogate biomarkers for tumor response to AAG therapy. The generalpurpose of CT imaging is to follow tumor size changes over time.However, AAG therapy frequently fails to shrink tumors early in thecourse of therapy, so reliance upon size measurements alone to indicateresponse to AAG therapy is insufficient to serve as a surrogatebiomarker for tumor response or survival. Thus, post-processing of CTimages may hold some promise for identifying surrogate biomarkers fortumor response to AAG therapy.

Newer CT imaging criteria have been developed to assess tumor responseto AAG therapy, including Morphology Assessment Size and Structure(MASS) Criteria. MASS Criteria includes evaluation of contrast-enhancedCT images for the presence or absence of marked central necrosis,defined as greater than 50% (as subjectively determined by the user) ofthe enhancing central portion of a predominantly solid mass subjectivelychanging to near fluid attenuation (necrosis) after treatment. Inaddition, MASS Criteria includes evaluation for changes in tumor size(using single slice length measurements of target lesions) and changesin tumor attenuation. In applying MASS Criteria, the user assesses formarked decreased attenuation in one or more target lesions, referring toone or more target lesions showing a greater than 40 Hounsfield Unit(HU) decrease in mean attenuation following AAG therapy, as compared tobaseline pre-therapy contrast-enhanced CT imaging.

Another commonly used CT imaging criteria for assessing tumor responseto AAG includes Choi Criteria, which is most commonly used in metastaticgastrointestinal tumor treated with AAG therapy. Choi Criteria includesevaluation for changes in tumor size (using a single slice lengthmeasurement of target lesion) and changes in mean tumor attenuation. ForChoi Criteria, the mean attenuation of each target lesion in HounsfieldUnits is measured, and an arithmetic mean of all target lesions is usedto calculate the final mean tumor attenuation. If the overall decreasein the final mean tumor attenuation is >15% compared to the baselinepre-therapy CT scan, then the patient is said to have had a PartialResponse to therapy.

Both MASS Criteria and Choi Criteria utilize routine contrast-enhancedCT images for tumor response assessment to AAG, and contrast-enhanced CTimaging is widely available and commonly used in the assessment of thesepatients. Furthermore, the strength in these newer CT imaging criteriais that they utilize multiple CT imaging findings to evaluate tumorresponse to AAG therapy, thereby better predicting tumor response to AAGtherapy than by evaluation of tumor size alone, such as per RECIST.

Tumor enhancement or attenuation on contrast-enhanced CT images isrelated to tumor vascularity, and both MASS Criteria and Choi Criteriainclude measurements of mean tumor attenuation, though objective tumorresponse is defined differently. There is little to no additionalexpense and no additional radiation required to make the tumor size ormean attenuation measurements. The process of making these measurementsis standardized and relatively easy to do with most picture archivingand communications systems. MASS Criteria is perhaps furtherstrengthened because it is includes evaluation for development of tumornecrosis, a natural consequence of successful AAG therapy.

However, neither MASS Criteria nor Choi Criteria is the solution to thelack of an ideal response criterion for determining and predicting tumorresponse to AAG therapy. A weakness of MASS Criteria is that assessmentfor marked tumor necrosis is subjective and thereby subject to reducedreproducibility. The weaknesses of both MASS Criteria and Choi Criteriaare that tumor vascularity is assessed by measuring changes in the meantumor attenuation of target lesions on contrast-enhanced CT images. Theintensity, or mean tumor attenuation, on contrast-enhanced CT is relatedto the amount of tumor vascularity, but the mean attenuation andenhancement of target lesions is also related to the amount ofadministered intravenous iodinated contrast, the rate of injection ofcontrast, cardiac output, and numerous CT scan parameters (e.g., kVp,mAs or mA, smoothing kernel, etc.). In addition, inadvertent inclusionof adjacent structures or tissues may skew the mean intensity value,such as inclusion of lung tissue in a region of interest around a lungnodule. Both MASS Criteria and Choi Criteria measure the intensity ofenhancement, but neither quantifies the amount of enhancing tissue.

In recent years, investigators have explored CT texture analysis. Whilethis technology can have different meanings, current forms of thistechnology objectively quantify overall pixel intensity and/or tumorheterogeneity. Common metrics used include mean, mean positive pixels,standard deviation, entropy, kurtosis, and skewness of target lesions.Each of the foregoing parameter can be measured before and afterprocessing the images with spatial band-pass filters having differentbandwidths. These methods are highly quantitative, but their ability topredict tumor response to AAG therapy is limited, and the biologicmechanism linking CT texture analysis to tumor response to AAG therapyis not understood. It is also unclear how measurements of multipletumors should be combined to indicate a final response to AAG therapy.

Both MASS Criteria and Choi Criteria and CT texture analysis fail todirectly quantify the amount of enhancing tumor (also known as thevascular tumor burden), and prior to this application, there were nomethods to directly quantify the vascular tumor burden from CT images.Tumors have both vascular components and avascular (necrotic)components. The component of tumor that is vascularized enhances, whilethe avascular portion does not and may be fluid density or have lowattenuation. The vascular tumor burden is thought to significantlydecrease in response to successful AAG therapy, though the total tumorsize often does not. Furthermore, an increase in vascular tumor burdenmay be a better indicator of AAG therapy, as it has the potential toprovide a quicker determination of AAG failure than waiting for newmetastases or an increase in total tumor size by >20%. Prior to thisapplication, there were no methods to directly quantify the vasculartumor burden prior to or after AAG therapy. There were also no methodsto simultaneously assess one or more tumor metrics as defined by anycombination of the known tumor response criteria, including RECIST, ChoiCriteria, MASS Criteria, and CT texture analysis, while employingmethods to reduce errors in tumor assessment and reduce the overall timeof interpretation compared to serial tumor assessment by the variouscriteria and CT texture analysis.

In clinical trials, it is often necessary to evaluate patients by avariety of imaging-based tumor response criteria. As each criterion hasdifferent standards and methods, it is quite challenging to minimizeerrors when evaluating patients using one or more of the knownimaging-based tumor response criteria. Furthermore, none of the existingtumor response criteria incorporate fully quantitative metrics thatdirectly capture tumor devascularization, the main effect ofanti-angiogenic therapy.

In clinical trials that include patients with advanced cancer treatedwith anti-angiogenic therapy, there is a need to simultaneously assesstumor response on cross-sectional images via a plurality of tumorresponse criteria. It is currently unclear which tumor responsecriterion is best for which tumor and which criterion is best pairedwith which anti-angiogenic therapy. Furthermore, there is a need todirectly quantify changes in the vascular tumor burden oncross-sectional images to effectively monitor response toanti-angiogenic therapy and to predict long-term response toanti-angiogenic therapy.

Vascular Tumor Burden as a Tumor Response Criterion

The Vascular Tumor Burden (VTB) Criteria, as disclosed and enabledherein, fill the need for an ideal response criterion for determiningand predicting tumor response to AAG therapy. As discussed below, theVTB Criteria provide a straightforward quantitative metric that isdirectly related to the main effect of AAG therapy, namely tumordevascularization, and is easy to use, is based on post-processing CTimages, which makes it widely available, is widely applicable, isamenable to high throughput, is inexpensive, and is highly reproducibleacross multiple different institutions and readers.

In general, the VTB is defined by a quantitative measure of pixelintensity within a target lesion. More particularly, to determine VTB,the total range of pixel intensities for a given target lesion isdetermined and the range of pixel intensities is restricted to a firstrestricted range of pixel intensities, wherein the first restrictedrange of pixel intensities corresponds to a first subset of pixelintensities representative of vascularized tumor. Stated another way,deriving the VTB comprises determining an area (or volume) of pixelswithin the first restricted range of pixel intensities. For example, therestricted range of pixel intensities for a CT image may be from +40 to+300 HU for a solid metastasis or lymph node, −100 to +300 HU for a lungmetastasis, or any range of pixel intensities. In fact, the range ofpixel intensities used to define the vascular tumor burden may differfor unenhanced and contrast-enhanced images and may differ by locationof the target lesions (e.g. liver versus lung).

For example, when injected radiocontrast is present in a CT image, therestricted range of pixel intensities may be defined from about +1 HU toabout +500 HU or from about +80 HU to about +200 HU, more preferablyfrom about +20 HU to about +450 HU or from about +20 HU to about +250HU, from about +25 HU to about +400 HU or from about +30 HU to about+350 HU or from about +30 HU to about +200 HU or from about +50 HU toabout +450 HU or from about +60 HU to about +480 HU, and most preferablyfrom about +35 HU to about +325 HU or from about +40 HU to about +300HU.

As a further example, when injected radiocontrast is absent from a CTimage, the restricted range of pixel intensities may be defined fromabout +1 HU to about +500 HU or from about +1 to about +200 HU or fromabout +50 HU to about +200 HU, more preferably from about +5 HU to about+425 HU or from about +5 HU to about +225 HU, or from about +10 HU toabout +400 HU or from about +10 HU to about +250 HU or from about +15 HUto about +350 HU or from about +40 HU to about +300 HU, and mostpreferably from about +20 HU to about +300 HU or from about +25 HU toabout +275 HU.

The purpose of the restricted range of pixel intensities is to strictlydefine the vascular tumor burden and to exclude structures such as air,fat, fluid, necrotic tumor, nonenhancing tumor, dense calcification,intensely enhancing vascular structures, metal, foreign bodies and/orimage artifacts that typically have pixel intensities above or below thespecified range of pixel intensities designed to capture the VTB.Accordingly, any conceivable range that effects that purpose is found tobe within the scope of this disclosure.

By defining the VTB by the quantitative measure of a range of pixelintensities derived from post-processing digital radiologiccross-sectional images, the VTB can be determined easily, inexpensively,is widely available, is widely applicable, is amenable to highthroughput, and is highly reproducible across multiple differentinstitutions and readers. Further, the VTB can be leveraged inlongitudinal studies to derive a decrease, increase, or no change in thevascularization of a given tumor, which may help to predict the tumorresponse to anti-cancer therapy, including AAG therapy.

Systems for Determining Objective Tumor Response

Referring now to FIG. 1, depicted is a schematic representation of asystem for determining an objective tumor response to an anti-cancertherapy using one or more cross-sectional images according to one ormore embodiments of the present disclosure. FIG. 1, generally, includesa computing system 100 configured to determine an objective tumorresponse. The computing system 100 may receive one or morecross-sectional images 102 from a radiologic device 104. In someembodiments, the radiologic device and the computing system arephysically connected and the one or more cross-sectional images 102 aretransferred via the physical connection. In other embodiments, thecomputing system 100 receives the one or more cross-sectional images 102from the radiologic device 104 via a network 130 digitally connectingthe radiologic device to the computing system 100. The network 130 maybe a private network such as an intranet of a hospital or the network130 may be a public network such as the Internet.

In some embodiments, the radiologic device comprises any device thatgenerates one or more cross-sectional images obtained by at least oneof: x-ray computed tomography, computed tomography perfusion (CTP)imaging, positron emission tomography (PET), single-photon emissioncomputed tomography (SPECT), or magnetic resonance imaging (MRI).Consequently, in some embodiments, the one or more cross-sectionalimages comprise: CT images, CTP images, CAT scan images, PET images,SPECT images, or MRI images.

Upon receiving the one or more cross-sectional images 102, the computingsystem 100 may store the one or more cross-sectional images 102 in aprimary database 116 or a hardware storage device 112 for later accessor may process one or more of the of the one or more cross-sectionalimages 102. By processing any of the one or more cross-sectional images102, the computing system 100 identifies one or more target lesionswithin the one or more cross-sectional images 102. The one or moretarget lesions are identified according to requirements disclosed by oneor more pre-defined tumor response criteria stored within computingsystem 100. In some embodiments, the one or more pre-defined tumorresponse criteria may be user-defined, or it may be defaulted to aparticular tumor response criterion such as, for example, the RECIST 1.1Criteria. In yet other embodiments, the computing system 100 determineswhich tumor response criteria to use based on one or more data withinthe one or more cross-sectional images 102 such as, for example, theanatomical location represented in the one or more cross-sectionalimages 102 or by the presence or absence of injected radiocontrast inthe one or more cross-sectional images 102.

Upon identifying the one or more target lesions from the one or morecross-sectional images 102, the computing system may, in someembodiments, select a particular slice for each of the one or moretarget lesions. The slice may be selected by, for example, determiningthe slice from the one or more cross-sectional images 102 where thegiven lesion has the longest length measurement. Other lesioncharacteristics may be used in selecting the slice, including, forexample, the length of the short axis or the area of the lesion or theslice with the largest amount of vascular tumor. Selecting a particularslice for each of the one or more target lesions may be carried out byan image processing module 110 of the computing system 100 and may bedone automatically or may be selected by a user through I/O deviceinterface 106.

In some embodiments, the computing system 100 analyzes at least one ofthe one or more target lesions with an image processing module 110,wherein for the at least one of the one or more target lesions, theimage processing module 110 is configured to perform one or more of thefollowing: identify a total range of pixel intensities within the targetlesion; restrict the total range of pixel intensities to a firstrestricted range of pixel intensities, wherein the first restrictedrange of pixel intensities corresponds to a first subset of pixelintensities representative of vascularized tumor; and determine one ormore lesion metrics. In some embodiments, the image processing module110 is configured to perform only two of the foregoing. In someembodiments, the image processing module 110 is specifically configuredto identify a total range of pixel intensities within the target lesionand restrict the total range of pixel intensities to a first restrictedrange of pixel intensities.

In any of the foregoing embodiments where the image processing module110 is configured to restrict the range of pixel intensities, therestricted range of pixel intensities may be set to any range of pixelintensities automatically determined by the computing system 100 or asdirected by the user through I/O device interface 106. Additionally oralternatively, the restricted range of pixel intensities may be any ofthe ranges discussed above with respect to deriving the VTB and may beinformed by one or more criteria such as anatomical location of thetumor or the presence or absence of injected radiocontrast.

In some embodiments, the computing system 100 compensates for thepresence or absence of injected radiocontrast through, for example,image processing module 110, in any of the one or more cross-sectionalimages that comprise computed tomography images, the compensatingcomprising defining the first restricted range of pixel intensities tobetween about +40 and about +300 Hounsfield units when injectedradiocontrast is present and defining the first restricted range ofpixel intensities to between about +20 and about +300 Hounsfield unitswhen injected radiocontrast is absent. The presence or absence of theradiocontrast may be automatically determined by the computing system100 (e.g., the image processing module 110 or the data processing module114 identifying one or more metadata associated with the one or morecross-sectional images being received at the computing system) or may,alternatively, be selected by a user at I/O device interface 106.

In some embodiments, the image processing module 110 determines one ormore lesion metrics. In some embodiments, the one or more lesion metricsare selected by a user at a user interface, such as the I/O userinterface 106 of computing system 100, and the image processing module110 calculates and/or determines the user-specified lesion metrics forthe at least one of the one or more target lesions. The one or morelesion metrics may include one or more lesion metrics selected from thegroup consisting of: a longest dimension length; a short axis dimensionlength; a longest dimension length of vascularized tumor; a pixel areaof the at least one of the one or more target lesions; a pixel areawithin the first restricted range; a pixel area within the secondrestricted range; a mean value of pixel intensities within the totalrange of pixel intensities; a mean value of pixel intensities within thefirst restricted range of pixel intensities; a histogram parameter,wherein the histogram parameter comprises a quantitative distribution ofpixel intensities in the at least one of the one or more target lesions;and a texture parameter, wherein the texture parameter comprises ageographic distribution of pixel intensities in the at least one of theone or more target lesions.

In some embodiments, the image processing module 110 or the computersystem 100, generally, may additionally be configured to: restrict thetotal range of pixel intensities to a second restricted range of pixelintensities, wherein the second restricted range of pixel intensitiescorresponds to a second subset of pixel intensities representative ofavascular (or necrotic) tumor; and derive a necrotic tumor burden forthe at least one of the one or more target lesions, wherein deriving thenecrotic tumor burden comprises determining an area or volume of pixelswithin the second restricted range of pixel intensities.

Restricting the total range of pixel intensities to a second restrictedrange of pixel intensities may include analogous compensation by thecomputing system based on the presence or absence of injectedradiocontrast when restricting the first range of pixel intensities.Additionally or alternatively, the second restricted range of pixelintensities may not overlap with the first restricted range of pixelintensities or may overlap by about 1, 3, 5, 7, 10, 15, 20, 40, or 80 HUon either the upper and/or lower bound of the first restricted range.Additionally or alternatively, the second restricted range may onlyrestrict the pixel intensities falling at or below the lower bound ofthe first restricted range. For example, if the first restricted rangeis between about +40 HU and about +300 HU, then the second restrictedrange may only restrict a subset (or the whole range) of HU values at orbelow about +40.

In some embodiments, analyzing the at least one of the one or moretarget lesions with an image processing module comprises analyzing afirst cross-sectional image and a second cross-sectional image of the atleast one of the one or more target lesions, wherein the firstcross-sectional image comprises digital medical image data of the atleast one of the one or more target lesions captured at a first point intime and the second cross-sectional image comprises digital medicalimage data of the at least one of the one or more target lesionscaptured at a second point in time, the second point in time beingchronologically after the first point in time; and evaluating the secondcross-sectional image with respect to the first cross-sectional image.

Having a temporal comparison between the at least one of the one or moretarget lesions (and any other target lesion) allows for changes inmetrics to be computed which detail changes in one or more lesionmetrics over time. These data may be informative in concluding whether atherapy is working. For example, if the change in data indicates thatthe area of a lesion is less at the second time point when compared tothe first time point, it may indicate that the whole tumor or acomponent of the tumor (e.g., the vascular tumor burden) is shrinkingand that the therapy is working. While not indicative, the data may,nonetheless, be informative.

In some embodiments, the computing system 100 of FIG. 1 may furtherderive a vascular tumor burden for the at least one of the one or moretarget lesions. This may be accomplished, for example, by dataprocessing module 114, where the pixel area in the first restrictedrange is calculated and provided as a quantitative indication of VTB.Additionally or alternatively, the computing system 100 may derive anecrotic tumor burden for the at least one of the one or more targetlesions. The second restricted range of pixel intensities may be used todefine a necrotic tumor burden in an analogous fashion to how a VTB isdetermined using the first restricted range of pixel intensities. Thatis, in some embodiments, the area (or volume) of pixel intensitieswithin the second restricted range of pixel intensities may be used toderive a necrotic tumor burden, which can similarly be calculated atdata processing module 114 of computing system 100.

In either of the foregoing instances—deriving the vascular tumor burdenor deriving the necrotic tumor burden—each may be accomplished withincomputing system 100 by one or more hardware processors 108 and/or by adata processing module 114 and may be derived in any manner or analogousmanner previously described above for defining and/or determining theVTB.

In some embodiments, the computing system 100, whether through hardwareprocessor 108 or data processor module 114, or a combination thereof,determines the objective tumor response for the at least one of the oneor more target lesions wherein the objective tumor response is based onthe vascular tumor burden. In some embodiments, therefore, comparativemeasures of the VTB at different time points may be used to determinethe objective tumor response. In other embodiments, the objective tumorresponse for the at least one of the one or more target lesions may bebased on the VTB and one or more tumor response criteria, as definedherein. That is, in some embodiments, the objective tumor response maybe the resultant combination of tumor response criteria. In yet otherembodiments, the objective tumor response is based on one or more tumorresponse criteria, wherein the one or more tumor response criteriacomprise the one or more tumor response criteria as defined herein.

Importantly, and as will be described in greater detail with respect tovarious disclosed methods, determining the objective tumor response andany of the other disclosed derivations, determinations, or analysesperformed in embodiments disclosing the computing system 100 of FIG. 1may be done automatically by the computing system, may be performedafter receiving one or more user instructions at a computer interface,or may be performed as a combination thereof. In some embodiments, thecomputer acts as a guide for the user, leading the user to determiningan objective tumor response from one or more cross-sectional images andpreventing errors in target lesion selection, data transfer,mathematical processing, response classification, and data and imagearchival. In other embodiments, the computing system automaticallycomputes and derives data such as one or more of the vascular tumorburden, the necrotic tumor burden, and the objective tumor response. Thecomputing system may, in some embodiments, automatically calculate ordetermine a plurality of tumor response criteria and may selectively,reiteratively, or automatically calculate any lesion metrics, tumorresponse criteria, VTB, or other data in response to a user editing orchanging one or more criteria at a user interface.

Some distinct advantages of automating the process of determining anobjective tumor response is that it reduces the likelihood of humanerror, increases reproducibility, and provides a quantitative measurefor certain tumor response criteria, such as the VTB Criteria, thatwould otherwise be left to subjective guesswork. Further, computerautomation allows for simultaneous measurement of a plurality of tumormetrics (including VTB), simultaneous assessment of lesions by multipletumor response criteria, reduced read times, automated mathematicalcalculations of summary data, automated generation of key images,automated archiving of regions of interest data, automated archiving oftumor metric data, and instant generation of a summary report.

The latter two elements will now be discussed with continued referenceto FIG. 1. In some embodiments, one or more data determined, derived,and/or analyzed at computing system 100 may be exported by export module118 and archived in a storage database system 124, wherein storagedatabase system 124 comprises persistent memory. The storage databasesystem 124 may be remote from the computing system such that send theone or more data to be archived at storage database 124 may necessitatetransferring the data over a network 126. The network 126 may be thesame network as networks 128 and 130, but in some embodiments it is adifferent network.

For example, in some embodiments, the computer system, such as computersystem 100, may also include computer executable instructions that areexecutable by one or more processors to configure the computer system toexport and store the following in a database having persistent memory,similar to storage database 124: one or more data representing the oneor more of lesion metrics and the determined objective tumor response;one or more cross-sectional images comprising the one or more targetlesions, wherein the one or more cross-sectional images are exported asone or more portable network graphics files; and one or more datacomprising the one or more target lesions, wherein the one or more dataare exported in a Digital Imaging and Communications in Medicine (DICOM)format.

In some embodiments, the export module 118 exports data in specificformats as exemplified by the foregoing. However, in some embodiments,image files may be exported in any of a user-defined image file format,including without limitation, JPEG, TIFF, GIF, BMP, SVG, or other imagefile formats known in the art, or as a graphic file format such as, forexample, PDF. Similarly, data comprising the one or more target lesionsmay be exported in any of text formats, image formats, graphic fileformats, or any other suitable file format known in the art.

In some embodiments, the export module 118 exports at least a portion ofthe data from computing system 100 to any of storage database system124, primary database 116, and/or directly to a user through I/O deviceinterface 106. In some embodiments, export module exports key images andessential data upon user-termination of a session with computing system100. Key images include any graphic or illustrations, as described inmore detail below, generated as a result of determining an objectivetumor response at computing system 100. Essential information includesany information generated as a result of determining an objective tumorresponse at computing system 100 and includes at least any of thefollowing: one or more target lesions, identification labels for the oneor more target lesions, derived VTB, determined objective tumorresponse, values indicating a first and/or a second restricted range ofpixel intensities, and one or more determined lesion metrics. In someembodiments the user may select at a user interface what information isessential information and what information and/or key images will beexported and stored.

In some embodiments, the exported data in storage database system 124may be compiled for statistical analysis to, for example, help informfuture treatment strategies. In some embodiments, the data, whenexported by export module 118, encrypts the data to protect anyconfidential patient information and may provide the user with a key,decryption algorithm, or the like to access the data at a later time.Additionally or alternatively, the data may be anonymized, removing anypatient specific identification from the exported data before storing ina database or exporting to a user.

In any of the foregoing embodiments described with respect to FIG. 1,and other additional embodiments of the present disclosure, a user mayaccess computing system 100 for determining an objective tumor responseto an anti-cancer therapy from computing device 120 and may do sothrough I/O device interface 106 of the computing system 100. The usermay access computing system 100 over network 128, which may be the sameor a different network than those described for networks 126 and 130.

In one embodiment, the computing device and the computing system are thesame. This may, for example, occur when one or more method stepsembodied by one or more portions of FIG. 1 and its accompanyingdescription provided herein comprise a computer programmable product.

In some embodiments, the user may interact with computing system 100before, during, and/or after the computing system is determining anobjective tumor response to an anti-cancer therapy using owe or morecross-sectional images. In doing so, the computing system 100 may alsoinclude computer executable instructions that are executable by one ormore processors to configure the computer system to receive one or moreinputs, wherein the one or more more inputs comprise: a determination ofinjected radiocontrast in any of the one or more cross-sectional images;data related to a response of one or more non-target lesions; a presenceof one or more new metastases; a label for the at least one of the oneor more target lesions, wherein the label comprises information selectedfrom the group consisting of: a lesion type of the at least one of theone or more target lesions, wherein lesion type can be a primary tumor,metastasis, or lymph node; an anatomical location of the at least one ofthe one or more target lesions; and any combination there of; and alabel for the one or more non-target lesions, wherein the labelcomprises information selected from the group consisting of: a lesiontype of the one or more non-target lesions, an anatomical location ofthe one or more non-target lesions, and any combination thereof.

The user may interact with the computing system 100 of FIG. 1 andreceive a customizable summary image 122 from computing system 100. Forthe purposes of this disclosure a customizable summary image 122 issynonymous and interchangeable with a detailed summary display 122. Thecustomizable summary image may comprise a first illustration, whereinthe first illustration comprises an illustration of the at least one ofthe one or more target lesions at the first point in time, and a secondillustration, wherein the second illustration comprises an illustrationof the at least one of the one or more target lesions at the secondpoint in time; and at least one additional component selected from thegroup consisting of: a third illustration, wherein the thirdillustration comprises an illustration of the vascular tumor burden atthe first point in time; a fourth illustration, wherein the fourthillustration comprises an illustration of the vascular tumor burden atthe second point in time; a fifth illustration, wherein the fifthillustration comprises an illustration of the necrotic tumor burden atthe first point in time; a sixth illustration, wherein the sixthillustration comprises an illustration of the necrotic tumor burden atthe second point in time; a seventh illustration, wherein the seventhillustration comprises an illustration of a total tumor burden at thefirst point in time; an eighth illustration, wherein the eighthillustration comprises an illustration of the total tumor burden at thesecond point in time; a first graphical display illustrating one or morechanges in vascularized tumor between the first point in time and thesecond point in time; a second graphical display illustrating one ormore changes in necrotic tumor between the first point in time and thesecond point in time; a numeric value representing at least one of apercent change, an average change, or an absolute change in the one ormore lesion metrics; a first indication of objective response, whereinthe first indication comprises an indication that the at least one ofthe one or more target lesions is responding or not responding toanti-cancer therapy; a second indication of objective response, whereinthe second indication of objective response comprises an indication thatthe one or more non-target lesions is responding or not responding toanti-cancer therapy; a first readable text, wherein the first readabletext comprises the one or more lesion metrics; a second readable text,wherein the second readable text comprises the presence or absence ofnew metastases; a third readable text, wherein the third readable textcomprises the vascular tumor burden for the at least one of the one ormore target lesions; a fourth readable text, wherein the fourth readabletext comprises the necrotic tumor burden for the at least one of the oneor more target lesions; and a fifth readable text, wherein the fifthreadable text comprises the objective tumor response for the one or moretarget lesions as determined by the one or more tumor response criteria.

In some embodiments, the determination of which of the foregoingadditional components will be displayed at the first illustration in thesecond illustration in the customizable summary image is dependent uponcomputing device 100. For example, computing device may have a standardand/or pre-determined display comprising one or more additionalcomponents. A user may, before or after viewing the customizable summaryimage adjust and/or customize the image to suit their needs.

While FIG. 1 depicts several independent modules 106, 108, 110, 114,116, 118, one will understand the characterization of a module is atleast somewhat arbitrary. In at least one implementation, the modules106, 108, 110, 114, 116, 118 of FIG. 1 may be combined, divided, orexcluded in configurations other than that which is shown. As usedherein, the individual modules 106, 108, 110, 114, 116, 118 are providedfor the sake of clarity and explanation and are not intended to belimiting.

Referring now to FIGS. 2-5, illustrated are embodiments of computerinterfaces comprising a customizable summary image according to one ormore embodiments of the present disclosure. FIGS. 2-3 illustrateschematic representations of the actual embodiments of customizablesummary images depicted in FIGS. 4-5, respectively.

FIG. 2 illustrates a computer interface comprising a customizablesummary image 150. Included therein are a first illustration 152 and asecond illustration on 154, where in the first illustration comprises atleast one of the one or more target lesions 162. As depicted, there areat least three target lesions 162 a, 162 b, and 162 n, where n is aninteger greater than 2. Though depicted as at least three targetlesions, it will be appreciated that the first illustration may depictas few as one target lesion and that the number of target lesions in thesecond illustration may be greater or less than the target lesions inthe first illustration. If new metastases arise between the first andsecond point of time 156, 158, there may be a greater number of targetlesions in the second illustration than in the first. If, however, theanti-cancer therapy is working, there may be two time points in themonitored therapy wherein the second illustration has fewer targetlesions as compared to the first illustration due to a significantreduction in size of the tumor and/or destruction of the tumor.

Referring back to FIG. 2, each target lesion 162, 164 is associated witha label 172, 174 in so far that each of the target lesions depicted 162a, 162 b, 162 c, 164 a, 164 b, 164 c is labeled respectively 172 a, 172b, 172 c, 174 a, 174 b, 174 c. In some embodiments, label 172 is thesame as label 174 because the second illustration 154 depicts the sametarget lesions illustrated in the first illustration 152, only a at alater point in time. In addition to the first and second illustrations152, 154, the customizable summary FIG. 150 further includes targetlesion metrics (Time0) 166, target lesion metrics (Time1) 168, targetlesion change data 178, and objective tumor response data 180. Thecustomizable summary FIG. 150 may also contain a unique ID 160, whichmay include patient identification information such as the patient'sname, medical record number, date of birth, a coded identificationnumber, or an anonymous patient ID number.

On the other hand, other embodiments of the present disclosure providethat the determination of which component(s) of the foregoing additionalcomponents will be displayed with the first illustration and the secondillustration in the customizable summary image 122 is dependent upon oneor more user selections at a user interface and/or at I/O device 106 ofthe computing system 100. The user may, in some embodiments, select oneor two components, a plurality of components, or all of the componentsto be displayed with the first illustration and the second illustrationin the customizable summary image 122.

Additionally or alternatively, the customizable summary image 122 maydynamically change as a user interacts with computing system 100 througha user interface, such as through I/O device interface 106. In someembodiments, the customizable summary image is a user interface and oneor more of the displayed objects (e.g., objects associated withreference numbers 152, 154, 156, 158, 160, 162, 164, 166, 168, 178, 180)may be movable by the user from their current depicted position to adifferent position or they may be removed altogether. As a non-limitingexample, the customizable summary image may include the thirdillustration overlain atop of the first illustration, and upon userselection through interface 106, the third illustration may be removedand replaced with a fifth illustration. The user may view this change inreal time. Additionally or alternatively, the user may maintain thethird illustration and add a fourth illustration to the customizablesummary image. The computing system 100 is in many embodimentsdynamically responsive to user inputs, and in doing so, outputs such asthe customizable summary image 122 may be similarly dynamic in theirdisplayed content. This gives the user, who may be a physician, theability to explore various tumor response criteria and various lesionmetrics as they relate to the one or more cross-sectional images. Inthis way, the physician's workload is streamlined, taking significantlyless time to view and analyze imaging results, and enabling thephysician to view a plurality of tumor response criteria simultaneously,including VTB, so a more-informed decision can be made with reference tothe efficacy of the patient's therapy, which is particular useful withrespect to patients undergoing AAG therapy.

Referring now to FIG. 4, depicted is an implementation of the computerinterface of FIG. 2. Depicted are four target lesions 262 a, 262 b, 262c, 262 d at time point CT0 and the same four target lesions 264 a, 264b, 264 c, 264 d at time point CT1. Each target lesion set includestarget lesion metrics 266, 268 at each of the respective time points CT0and CT1 and respectively represent a first illustration 256 and a secondillustration 258. Also included in user interface/customizable summaryimage 250 are labels 272 a, 272 b, 272 c, 272 d indicating an anatomicallocation of the target lesion and providing a target number (e.g.,Target 1, Target 2, etc.) as well as target lesion change data 278,which as depicted includes a percent change in: length, meanattenuation, total lesion area, VTB area, and necrosis area betweentarget lesions at time points CT0 and CT1. Lastly, the customizablesummary image 250 includes objective tumor response data 280,particularly, the objective tumor response data 280 include tumorresponse data from one or more tumor response criteria, namely RECIST,10% Tumor Diameter Shrinkage Criteria (10% Criteria), Choi Criteria,Modified Choi Criteria, MASS Criteria, and VTB Criteria.

Similar to FIG. 2, in some embodiments, a user may dynamically changethe order or layout of the customizable display image or may selectdifferent lesion metrics or different tumor response criteria todisplay.

Referring now to FIG. 3, depicted is a schematic representation of acomputer interface comprising a customizable summary image 200 accordingto one or more embodiments of the present disclosure. FIG. 3 is similarto FIG. 2 and maintains some of the same items, such as: target lesionmetrics (216 and 218, respectively for Time0 206 and Time1 208), targetlesion change data 220, objective tumor response data 230, and a uniquepatient or customizable summary image ID 210. The main differencebetween FIG. 3 and FIG. 2 is the presence of additional illustrationsfor each time point. The first illustration 202 and second illustration204 remain, and they maintain a similar temporal relationship asdepicted in FIG. 2. Illustration 203 may be any of the third, fifth, orseventh illustrations and 205 may be any of the fourth, sixth, or eighthillustrations listed as one of the at least one additional component toaccompany illustrations one and two in the customizable summary image.For each hypothetical pairing, the result is the same: illustration 203depicts the same target lesions (i.e., 222 a, 222 b, 222 n) at the sametime point as those target lesions (212 a, 212 b, 212 n) in the firstillustration 202 but demonstrates those target lesions through the lensof VTB, necrotic tumor burden, or total tumor burden. For example,target lesion 212 a is the same target lesion as target lesion 222 a,but target lesion 212 a shows the target lesion as originally capturedfrom the one or more cross-sectional images, without anypost-processing, whereas target lesion 222 a shows target lesion 212 awherein some post-processing to the target lesion has taken place,perhaps being overlaid with a texture or color mask that depicts VTB.

The same or similar foregoing relationship may hold true for targetlesions 214 and 215 of the second illustration 204 and illustration205—which may be any one or more of the fourth, sixth, or eighthillustrations. Again, the texture and/or color associated with targetlesions 215 a, 215 b, and 215 n may be indicative of the VTB, necrotictumor burden, or total tumor burden associated with target lesions 214a, 214 b, and 214 n, respectively.

Referring now to FIG. 5, depicted is an implementation of the computerinterface of FIG. 3 according to one or more embodiments of the presentdisclosure. FIGS. 3 and 5 maintain a similar relationship to that whichwas demonstrated above between FIGS. 2 and 4. With reference to FIG. 5,there are five target lesions depicted for each of the illustrations302, 303, 304, and 305, each target lesion 312, 313, 314, 315 having alabel 322 a 322 b, 322 c, 322 d, 322 e that recites the anatomicallocation (e.g., lung, peritoneal, etc.) of the target lesion along witha target lesion ID (e.g., Target 1, Target 2, etc.). The abbreviation LNindicates that target lesion 322 c is a lymph node, differentiating itfrom the other target lesions, which are metastases. Just as in FIGS.2-4, FIG. 5 illustrates target lesion metrics 316, 318 for targetlesions at respective time points CT0 and CT1 and target lesion changedata 320, which as depicted includes a percent change in: length, meanattenuation, total lesion area, and VTB area between target lesions attime points CT0 and CT1. Lastly, the customizable summary image 300includes objective tumor response data 330, particularly, the objectivetumor response data 330 include tumor response data from one or moretumor response criteria, namely RECIST, Choi Criteria, MASS Criteria,and VTB Criteria.

Similar to FIGS. 2-4, in some embodiments, a user may dynamically changethe order or layout of the customizable display image 300 of FIG. 5 ormay select different lesion metrics 316, 318 or different tumor responsecriteria 330 to display.

Thus, implementations of the present invention may extend to computersystems comprising one or more computer readable hardware storage devicethat comprise computer executable instructions executable by at leastone of one or more processors to cause the computer system to determinean objective tumor response to an anti-cancer therapy using one or morecross-sectional images. In particular, the computer systems may comprisecomputer-executable instructions that are executable by one or moreprocessors to configure the computer system to perform the following:receive one or more cross-sectional images that comprise one or morecross-sectional slices of digital medical image data from a radiologicdevice; identify one or more target lesions within the one or morecross-sectional images; analyze at least one of the one or more targetlesions with an image processing module, wherein for the at least one ofthe one or more target lesions, the image processing module isconfigured to: identify a total range of pixel intensities, restrict thetotal range of pixel intensities to a first restricted range of pixelintensities, wherein the first restricted range of pixel intensitiescorresponds to a first subset of pixel intensities representative ofvascularized tumor, and determine one or more lesion metrics; derive avascular tumor burden for the at least one of the one or more targetlesions; and determine the objective tumor response for the at least oneof the one or more target lesions wherein the objective tumor responseis based on the vascular tumor burden.

Additionally or alternatively, implementations of the present inventionmay extend to computer systems comprising the foregoing computer systemand which additionally comprises computer executable instructionsexecutable by at least one of one or more processors to cause thecomputer system to select a slice from the one or more cross-sectionalimages for each of the one or more target lesions, wherein the sliceselected for each of the one or more cross-sectional images comprisesthe slice having the longest dimension length for each of the one ormore target lesions.

Additionally or alternatively, implementations of the present inventionmay extend to computer systems comprising one or more computer readablehardware storage device that comprise computer executable instructionsexecutable by at least one of one or more processors to cause thecomputer system to perform the following: receive one or morecross-sectional images that comprise one or more cross-sectional slicesof digital medical image data from a radiologic device; identify one ormore target lesions within the one or more cross-sectional images;analyze at least one of the one or more target lesions with an imageprocessing module, wherein for the at least one of the one or moretarget lesions, the image processing module is configured to: identify atotal range of pixel intensities, restrict the total range of pixelintensities to a first restricted range of pixel intensities, whereinthe first restricted range of pixel intensities corresponds to a firstsubset of pixel intensities representative of vascularized tumor, andrestrict the total range of pixel intensities to a second restrictedrange of pixel intensities, wherein the second restricted range of pixelintensities corresponds to a second subset of pixel intensitiesrepresentative of necrotic tumor; derive a vascular tumor burden for theat least one of the one or more target lesions; derive a necrotic tumorburden for the at least one of the one or more target lesions, whereinderiving the necrotic tumor burden comprises determining an area orvolume of pixels within the second restricted range of pixelintensities; determine the objective tumor response for the at least oneof the one or more target lesions, wherein the objective tumor responseis based on the derived vascular tumor burden and one or more tumorresponse criteria; and provide a customizable summary image to one ormore users, wherein the customizable summary image comprises a firstillustration, wherein the first illustration comprises an illustrationof the at least one of the one or more target lesions at the first pointin time, and a second illustration, wherein the second illustrationcomprises an illustration of the at least one of the one or more targetlesions at the second point in time; and at least one additionalcomponent selected from the group consisting of: a third illustration,wherein the third illustration comprises an illustration of the vasculartumor burden at the first point in time; a fourth illustration, whereinthe fourth illustration comprises an illustration of the vascular tumorburden at the second point in time; a fifth illustration, wherein thefifth illustration comprises an illustration of the necrotic tumorburden at the first point in time; a sixth illustration, wherein thesixth illustration comprises an illustration of the necrotic tumorburden at the second point in time; a seventh illustration, wherein theseventh illustration comprises an illustration of a total tumor burdenat the first point in time; an eighth illustration, wherein the eighthillustration comprises an illustration of the total tumor burden at thesecond point in time; a first graphical display illustrating one or morechanges in vascularized tumor between the first point in time and thesecond point in time; a second graphical display illustrating one ormore changes in necrotic tumor between the first point in time and thesecond point in time; a numeric value representing at least one of apercent change, an average change, or an absolute change in the one ormore lesion metrics; a first indication of objective response, whereinthe first indication comprises an indication that the at least one ofthe one or more target lesions is responding or not responding toanti-cancer therapy; a second indication of objective response, whereinthe second indication of objective response comprises an indication thatthe one or more non-target lesions is responding or not responding toanti-cancer therapy; a first readable text, wherein the first readabletext comprises the one or more lesion metrics; a second readable text,wherein the second readable text comprises the presence or absence ofnew metastases; a third readable text, wherein the third readable textcomprises the vascular tumor burden for the at least one of the one ormore target lesions; a fourth readable text, wherein the fourth readabletext comprises the necrotic tumor burden for the at least one of the oneor more target lesions; and a fifth readable text, wherein the fifthreadable text comprises the objective tumor response for the one or moretarget lesions as determined by the one or more tumor response criteria.

As disclosed in the above system, implementations of the presentdisclosure may extend to systems for determining an objective tumorresponse to anti-cancer therapy that determine both a vascular tumorburden and a necrotic tumor burden and which require the objective tumorresponse to be determined by the vascular tumor burden and one or moretumor response criteria.

Additionally or alternatively, implementations of the present inventionmay extend to computer systems comprising one or more computer readablehardware storage device that comprise computer executable instructionsexecutable by at least one of one or more processors to cause thecomputer system to perform the following: identify one or more targetlesions within the one or more cross-sectional images; analyze at leastone of the one or more target lesions with an image processing module,wherein for the at least one of the one or more target lesions, theimage processing module is configured to: identify a total range ofpixel intensities and restrict the total range of pixel intensities to afirst restricted range of pixel intensities; derive a vascular tumorburden for the at least one of the one or more target lesions; anddetermine the objective tumor response for the at least one of the oneor more target lesions, wherein the objective tumor response is based onthe results from one or more tumor response criteria.

As noted in the foregoing computer system, implementations of thecomputer system falling within the scope of this disclosure includecomputer systems that determine the objective tumor response for atleast one of the one or more target lesions, wherein the objective tumorresponse is based on the results from one or more tumor responsecriteria. The VTB may be included in the one or more tumor responsecriteria. In fact, the one or more tumor response criteria may beselected from the group consisting of: Response Evaluation Criteria inSolid Tumors (RECIST) 1.0, RECIST 1.1, modified RECIST, World HealthOrganization (WHO) Criteria, 10% Tumor Diameter Shrinkage Criteria, ChoiCriteria, Modified Choi Criteria, Morphology Attenuation Size andStructure (MASS) Criteria, Immune-related Response Criteria, ChesonCriteria, lymphoma response criteria, Revised Response Criteria forMalignant Lymphoma, Positron Emission Tomography Response Criteria inSolid Tumors (PERCIST), Metabolic Response Criteria, EuropeanOrganization for Research and Treatment of Cancer (EORTC), Internationaluniform response criteria for multiple myeloma, Current ResponseCriteria for High-Grade Gliomas, MacDonald Criteria, Response Assessmentof Neuro-Oncology (RANO) Criteria, Vascular Tumor Burden (VTB) Criteria,and computed tomography texture analysis criteria.

In some embodiments, a noise reduction filter may be applied to thedigital medical image data and/or to any selected subset or portionthereof. Further, in some embodiments, a smoothing algorithm is appliedto the digital medical image data. Moreover in some embodiments, thesmoothing algorithm may be Gaussian, additive smoothing, butterworthfilter, digital filter, kalman filter, kernel smoother, laplaciansmoothing, stretched grid method, low-pass filter, local regression,smoothing spline, moving average, or exponential smoothing. In someembodiments, the smoothing algorithm comprises a Gaussian smoothingalgorithm. In some embodiments, the smoothing algorithm is applied tothe digital medical image data or to any portion thereof. In someembodiments, the smoothing algorithm is applied only to the region ofinterest. The sigma value for the Gaussian smoothing algorithm may beincreased or decreased by the user, thereby increasing or decreasing theamount of smoothing, until the amount of smoothing is optimized. Inother embodiments, other image processing algorithms may include aFourier transformation of the image data or processing the data by pointprocessing operations, spatial filter operations (linear or non-linear),histogram processing operations, contrast-stretching transformation,image logarithmic transformation, power law transformation, preciselinear transformation, gray level slicing transformation, bit planeslicing, or pseudo coloring operations.

In some foregoing and forthcoming embodiments, the systems and methodsof the present disclosure include a step deriving a VTB and/orextracting a VTB, wherein the VTB is defined as an area (or volume) ofpixels within the first restricted range of pixel intensities in thedigital medical image data. The step of deriving a VTB and/or extractinga VTB may include, for example, calculating a pixel value for each ofthe pixels in the first restricted range of pixel intensities from thedigital medical image data and/or measuring a pixel value for each ofthe pixels in the first restricted range of pixel intensities from thedigital medical image data.

Computer-Implemented Methods and Computer Interfaces for DeterminingObjective Tumor Response

FIGS. 1-5 and the corresponding text illustrate or otherwise describeone or more components, modules, mechanisms and/or detailed summarydisplays (also referred to herein as customizable summary images) fordetermining an objective tumor response to an anti-cancer therapy usingone or more cross-sectional images. One will appreciate that embodimentsof the present invention can also be described in terms of methodscomprising one or more acts for accomplishing a particular result. Forexample, FIGS. 6-10, with the corresponding text, illustrates orotherwise describes a sequence of acts in a method for determining anobjective tumor response to an anti-cancer therapy using one or morecross-sectional images. The acts of FIGS. 6-10 are described below withreference to the components and modules illustrated in FIGS. 1-5.

FIG. 6 shows that a method 400 for generating and displaying a detailedsummary display can include an act 402 of receiving one or morecross-sectional images comprising one or more user-specified targetlesions that further comprise one or more regions of interest. Act 402can comprise receiving one or more cross-sectional images, wherein theone or more cross-sectional images comprise one or more cross-sectionalslices of digital medical image data from a radiologic device andidentifying one or more target lesions within the one or morecross-sectional images. For example, computing system 100 of FIG. 1 mayreceive one or more cross-sectional images 102 from radiologic device104, wherein the cross-sectional images 102 already includeuser-specified target lesions. Additionally or alternatively, a user mayspecify target lesions at computing system 100 through I/O deviceinterface 106.

Additionally or alternatively, the cross-sectional images 102 comprisingone or more user-specified target lesions may be received by computingsystem 100 from storage database system 124. In some embodiments, theone or more regions of interest within the user-specified target lesionsmay have been previously defined by a user and may include, for example,one or more restricted ranges of pixel intensities, whether chosen by auser or by a computing system.

FIG. 6 shows that the method 400 can also include act 404 ofautomatically extracting a vascular tumor burden and one or more lesionmetrics from the one or more user-specified target lesions. Act 404 cancomprise analyzing at least one of the one or more target lesions withan image processing module, wherein for the at least one of the one ormore target lesions, the image processing module is configured toidentify a total range of pixel intensities; restrict the total range ofpixel intensities to a first restricted range of pixel intensities,wherein the first restricted range of pixel intensities corresponds to afirst subset of pixel intensities representative of vascularized tumor;and determine one or more lesion metrics. Act 404 may further comprisederiving a vascular tumor burden for the at least one of the one or moretarget lesions.

In embodiments of the present invention, the image processing module 110of computing system 100 may automatically extract a VTB and one or morelesion metrics from the one or more user-specified target lesions.

In addition, FIG. 6 shows that the method 400 can include act 406 ofderiving an objective tumor response for the one or more user-specifiedtarget lesions based on the vascular tumor burden of the one or moreuser-specified target lesions. Act 406 can comprise determining theobjective tumor response for the at least one of the one or more targetlesions wherein the objective tumor response is based on the vasculartumor burden. In embodiments of the present invention, deriving anobjective tumor response may be done by hardware processors 108, imageprocessing modules 110, data processing modules 114, or any combinationthereof, or by computing system 100, generally. It may further includeor comprise deriving a VTB and/or extracting a VTB, using imageprocessing module 114, wherein the VTB is defined as an area (or volume)of pixels within a first restricted range of pixel intensities in theone or more user-specified target lesions. Deriving a VTB and/orextracting a VTB may include, for example, the image processing module110 calculating a pixel value for each of the pixels in the firstrestricted range of pixel intensities from the one or moreuser-specified target lesions and/or measuring a pixel value for each ofthe pixels in the first restricted range of pixel intensities from theone or more user-specified target lesions.

Furthermore, FIG. 6 shows that the method 400 can include act 408 ofgenerating and displaying a detailed summary display comprising theobjective tumor response for the one or more user-specified targetlesions and the one or more lesion metrics. Act 406 can comprisedisplaying a customizable summary image, wherein the customizablesummary image comprises a first illustration, a second illustration, andat least one additional component. For example, the detailed summarydisplay (i.e., the detailed summary image) may be displayed at computingsystem 100 through I/O device interface 106 or may be transferredthrough network 128 to computing device 120, where a user may view thedetailed summary image from a display associated with the computingdevice 120.

Referring now to FIG. 7, FIG. 7 shows that the method 400 for generatingand displaying a detailed summary display can include an act 422 ofreceiving one or more cross-sectional images and identifying an imagetype and a presence or absence of injected radiocontrast for the one ormore cross-sectional images. Act 422 comprises receiving one or morecross-sectional images, wherein the one or more cross-sectional imagescomprise one or more cross-sectional slices of digital medical imagedata from a radiologic device, and receiving one or more inputs, whereinthe one or more inputs comprise a determination of injectedradiocontrast in any of the one or more cross-sectional images.

For example, the computing system 100 may receive one or morecross-sectional images 102 from radiologic device 104. The computingdevice may identify an image type and a presence or absence of injectedradiocontrast from the one or more cross-sectional images 102 through animage processing module 110 that scans the one or more cross-sectionalimages 102 and makes a determination of the foregoing. Additionally oralternatively, computing system 100 may be pre-programmed to accept oneor more cross-sectional images 102 from a particular radiologic device104 such as, for example, CT images from a CT scanner, or computingsystem 100 may identify this information through data processing module114, which reads one or more meta data tags associated with images 102and which identify they type of image (e.g., a CT image, an MRI image, aPET image, a SPECT image, a CTP image, etc.) and the presence or absenceof injected radiocontrast.

The method of FIG. 7 can also include an act 424 of identifying one ormore target lesions from the one or more cross-sectional images andlabeling the one or more target lesions with a lesion type and ananatomical location. Act 424 can comprise identifying one or more targetlesions within the one or more cross-sectional images and receiving oneor more inputs, wherein the one or more inputs comprise a label for theat least one of the one or more target lesions, wherein the labelcomprises information selected from the group consisting of: a lesiontype of the at least one of the one or more target lesions, whereinlesion type can be a primary tumor, metastasis or lymph node; ananatomical location of the at least one of the one or more targetlesions; and any combination thereof.

For example, an image processing module 110 of the computing system 100may identify one or more target lesions from images 102 and a hardwareprocessor 108 or data processing module 114 or a combination thereof mayinclude one or more data with images 102 that comprise a label havinginformation related to the lesion type and anatomical location of theone or more target lesions. In some embodiments, receiving an input mayinclude the computing system receiving an input from one or more modulessuch as, for example, a data processing module that reads one or moremeta data tags comprising a lesion type and an anatomical location.

The method of FIG. 7 can also include an act 426 of segmenting the oneor more target lesions into regions of interest. Act 426 can compriseanalyzing at least one of the one or more target lesions with an imageprocessing module, wherein for the at least one of the one or moretarget lesions, the image processing module is configured to restrictthe total range of pixel intensities to a first restricted range ofpixel intensities. Though segmenting the one or more target lesions intoregions of interest may comprise restricting the total range of pixelintensities within the one or more target lesions to a restrictedrange—the region of interest—that does not mean that it is the only wayof segmenting the one or more target lesions into regions of interest orthat subsets of pixel intensities are the only regions of interest. Forexample, the computing device 100 may segment the one or more targetlesions into regions of interest using an image processing module 110where the regions of interest may be new metastases or non-targetregions of interest.

The method of FIG. 7 can also include an act 428 of extracting avascular tumor burden and one or more tumor metrics from the regions ofinterest. Act 428 can comprise analyzing at least one of the one or moretarget lesions with an image processing module, wherein for the at leastone of the one or more target lesions, the image processing module isconfigured to: identify a total range of pixel intensities; restrict thetotal range of pixel intensities to a first restricted range of pixelintensities, wherein the first restricted range of pixel intensitiescorresponds to a first subset of pixel intensities representative ofvascularized tumor; and determine one or more lesion metrics. Act 428may further comprise deriving a vascular tumor burden for the at leastone of the one or more target lesions. For example, image processingmodule 114 may perform the foregoing as described above.

The method of FIG. 7 can also include an act 430 of calculating one ormore changes in the vascular tumor burden and the one or more tumormetrics. For example, data processing module 114 or hardware processors108 of computing system 100 may calculate changes in VTB and one or moretumor metrics. The changes referenced may be changes observed over timewith respect to the same lesion.

The method of FIG. 7 can also include an act 432 of deriving anobjective tumor response. Act 432 can comprise determining the objectivetumor response for the at least one of the one or more target lesions.Act 432 may additionally comprise, but in some embodiments may notfurther comprise, wherein the objective tumor response is based on thevascular tumor burden. For example, the objective tumor response may bederived from any of one or more tumor response criteria and therequisite one or more lesion metrics. Computing system 100 mayautomatically analyze each of the one or more lesions in light of theone or more tumor response criteria, collecting the requisite lesionmetrics to calculate the corresponding objective tumor response.Additionally or alternatively, the objective tumor response may bebased, at least in part, on the VTB as defined above.

The method of FIG. 7 can also include an act 434 of generating anddisplaying a detailed summary display comprising the objective tumorresponse and the one or more lesion metrics. Act 434 can comprisedisplaying a customizable summary image, wherein the customizablesummary image comprises: a first illustration, wherein the firstillustration comprises an illustration of the at least one of the one ormore target lesions at the first point in time; a second illustration,wherein the second illustration comprises an illustration of the atleast one of the one or more target lesions at the second point in time;and at least one additional component. For example, the detailed summarydisplay (i.e., the detailed summary image) may be displayed at computingsystem 100 through I/O device interface 106 or may be transferredthrough network 128 to computing device 120, where a user may view thedetailed summary image from a display associated with the computingdevice 120.

Referring now to FIG. 8, FIG. 8 shows that the method 440 for generatingand displaying a detailed summary display can include an act 442 ofreceiving one or more cross-sectional images. Act 442 comprisesreceiving one or more cross-sectional images, wherein the one or morecross-sectional images comprise one or more cross-sectional slices ofdigital medical image data from a radiologic device. For example,computing system 100 may receive one or more cross-sectional images 102from radiologic device 104, or in some embodiments, computing system 100may receive one or more cross-sectional images from storage databasesystem 124. Additionally or alternatively, computing system 100 mayreceive one or more cross-sectional images from a combination ofradiologic device 104 and storage database system 124.

The method of FIG. 8 can also include an act 444 of receiving auser-identified image type and presence or absence of injectedradiocontrast for the one or more cross-sectional images. Act 444 cancomprise receiving one or more inputs, wherein the one or more inputscomprise a determination of injected radiocontrast in any of the one ormore cross-sectional images. For example, computing system 100 mayreceive user-identified image type and a presence or absence of injectedradiocontrast through I/O device interface 106 such that a user selectsthe foregoing using computing device 120, which may access the I/Odevice interface 106 of computing system 100 through network 126.Additionally or alternatively, user-identified image type and presenceor absence of injected radiocontrast may be received directly atcomputing system 100 by the user.

The method of FIG. 8 can also include an act 446 of receiving one ormore user-identified target lesions from the one or more cross-sectionalimages. Act 446 can comprise identifying one or more target lesionswithin the one or more cross-sectional images. For example, a user,through computing device 120 or directly at computing system 100 mayidentify target lesions from the one or more cross-sectional imagesusing I/O device interface 106.

The method of FIG. 8 can also include an act 448 of activating a regionof interest tool. For example, I/O device interface 106 may comprise aregion of interest tool that allows a user to select one or more regionsof interest. As an additional example, the activated region of interesttool may be a slider-based scale wherein the user may be able to selecta range of pixel intensities.

The method of FIG. 8 can also include an act 450 of monitoringuser-specified regions of interest selections and prompting the user,when necessary, to modify the region of interest or delete theassociated target lesion in accordance with a default or user-specifiedtumor response criteria. For example, one or more tumor responsecriteria may be pre-defined at computing system 100 or may be selectableby a user through I/O device interface 106. Each of the one or moretumor response criteria comprises particular rules and guidelines.Computing system 100 may be programmed to only accept target lesions—orother regions of interest—that conform to the rules and guidelines ofthe one or more tumor response criteria that are in force withincomputing system 100. In some embodiments, computing system 100 mayprompt the user to modify the region of interest or delete theassociated target lesion if it does not qualify as a region of interestaccording to whatever one or more tumor response criteria are being usedand the prompt may be sent to the user through I/O device interface 106.

The method of FIG. 8 can also include an act 452 of automaticallyextracting a vascular tumor burden and one or more tumor metrics fromthe user-specified regions of interest. Act 452 can comprise analyzingat least one of the one or more target lesions with an image processingmodule, wherein for the at least one of the one or more target lesions,the image processing module is configured to: identify a total range ofpixel intensities; restrict the total range of pixel intensities to afirst restricted range of pixel intensities, wherein the firstrestricted range of pixel intensities corresponds to a first subset ofpixel intensities representative of vascularized tumor; and determineone or more lesion metrics, and act 452 may further comprise deriving avascular tumor burden for the at least one of the one or more targetlesions. The VTB may be extracted or derived as described within thisdisclosure. In some embodiments, an image processing module 110automatically extracts the one or more tumor metrics automatically.

The method of FIG. 8 can also include an act 454 of receivinguser-indicated responses of non-target lesions and a presence or absenceof new metastases. Act 454 can comprise receiving one or more inputs,wherein the one or more inputs comprise a determination of injectedradiocontrast in any of the one or more cross-sectional images, datarelated to a response of one or more non-target lesions, and a presenceof one or more new metastases. For example, the user-indicated responsesand/or user inputs may be received at computing system 100 through I/Odevice interface 106.

The method of FIG. 8 can also include an act 456 of calculating one ormore changes in the vascular tumor burden and the one or more metrics.For example, the VTB and metrics associated with two temporally spacedlesions may be compared by data processing module 114 from which it maycalculate one or more changes.

The method of FIG. 8 can also include an act 458 of deriving anobjective tumor response according to at least one of a plurality oftumor response criteria. Act 458 can comprise determining the objectivetumor response for the at least one of the one or more target lesions.Act 458 may additionally comprise, but in some embodiments may notfurther comprise, wherein the objective tumor response is based on thevascular tumor burden. For example, the objective tumor response may bederived from any of one or more tumor response criteria and therequisite one or more lesion metrics. Computing system 100 mayautomatically analyze each of the one or more lesions in light of theone or more tumor response criteria, collecting the requisite lesionmetrics to calculate the corresponding objective tumor response.Additionally or alternatively, the objective tumor response may bebased, at least in part, on the VTB as defined above.

The method of FIG. 8 can also include an act 460 of generating anddisplaying a detailed summary display comprising the objective tumorresponse and the one or more lesion metrics. Act 460 can comprisedisplaying a customizable summary image, wherein the customizablesummary image comprises: a first illustration, wherein the firstillustration comprises an illustration of the at least one of the one ormore target lesions at the first point in time; a second illustration,wherein the second illustration comprises an illustration of the atleast one of the one or more target lesions at the second point in time;and at least one additional component. For example, the detailed summarydisplay (i.e., the detailed summary image) may be displayed at computingsystem 100 through I/O device interface 106 or may be transferredthrough network 128 to computing device 120, where a user may view thedetailed summary image from a display associated with the computingdevice 120.

Referring now to FIG. 9, FIG. 9 shows that the method 500 for reviewinga summary display can include an act 502 of accessing one or morecross-sectional images at a computing system. For example, a user mayaccess one or more cross-sectional images, which may comprise digitalmedical image data, at computing system 100 through I/O device interface106. The user may access interface 106 directly at computing system 100or over network 128 at computing device 120.

The method of FIG. 9 can also include an act 504 of identifying a firsttarget lesion. Act 504 can comprise identifying one or more targetlesions within the one or more cross-sectional images. For example, theuser may, through interactions with I/O device interface 106, identify afirst (or one or more) target lesions using any communications devicefor transmitting the user-identification to computing system 100, someexamples of which include a mouse, keyboard, touchscreen, or other meansof identifying and/or selecting information on a user interface that areknown in the art.

The method of FIG. 9 can also include an act 506 of segmenting the firsttarget lesion into a region of interest or reviewing a computersegmentation of the first target lesion into a region of interest. Forexample, the user may segment the first target lesion throughinteractions with interface 106 either directly at computing system 100or through network 128 on computing device 120. The user may segment thefirst target lesion by using a segmenting tool provided at interface 106or by any other means of segmenting or selecting features of objects orobjects, themselves within a digital image, including within digitalmedical image data. The user may additionally or alternatively review acomputer segmentation of the first target lesion using interface 106 andmay, in some embodiments, adjust the computer-derived segmentation.

The method of FIG. 9 can also include an act 508 of reviewing a summarydisplay comprising an objective tumor response and one or more lesionmetrics for the identified first target lesion. For example, the usermay, directly or indirectly review a summary display at the computingsystem 100 using interface 106. Example outputs of such a review summaryare provided in FIGS. 2-5.

With reference to FIG. 10, FIG. 10 shows that the method 520 forreviewing a summary display. The method 520 includes method acts 522,524, 526, 528, 530, 532, 534, 536, 538, and 540. Each of the foregoingmethods are performed by a user who may be performing each method act byinteracting directly or indirectly with a computing system fordetermining an objective tumor response to an anti-cancer therapy usingone or more cross-sectional images, an example embodiment of which isdepicted in FIG. 1. The user interaction with the computing system isperformed by interacting with a user interface that the computing systemmay respond to and which may cause dynamic changes to the resultingsummary display and/or to the one or more processes performed by thecomputing system in determining an objective tumor response. Forexample, a user may elect which of the one or more tumor responsecriteria for the computing system to use in determining an objectivetumor response. As a further example, the user may select a first orsecond (or a plurality) of restricted ranges of pixel intensities, whichthe computer may use in determining the VTB for one or more targetlesions.

In some embodiments of FIG. 10, the user, after reviewing the summarydisplay, may perform any or all of the method acts disclosed by FIG. 10or the user may perform other acts disclosed herein but not explicitlydisclosed in FIG. 10. These acts may cause the summary display todynamically change and/or adjust to the user acts. One benefit of thisdynamic interaction is that the user, who in some embodiments is aphysician or other healthcare provider, may quickly assess a patient'sresponse to anti-cancer therapy, including AAG therapy, and makeadjustments to the patient's treatment regimen as necessary and based onthe data. This is particularly beneficial due to the high error rateusing traditional methods for determining an objective tumor responsebased on any of the one or more tumor response criteria. This providesadditional technical benefits in that longitudinal comparisonsthroughout the patient's treatment history may be made or referencedquickly and accurately, and the results may further be used in large (orsmall) scale statistical analyses for predicting the efficacy of varioustherapies to one or more tumor types and/or tumor anatomical locations.

Referring now to the method of FIG. 10, it can include an act 522 ofaccessing one or more cross-sectional images at a computing system. Forexample, a user may access one or more cross-sectional images, which maycomprise digital medical image data, and may do so as described above.

The method of FIG. 10 can also include an act 524 of selecting an imagetype of the one or more cross-sectional images. For example, the usermay select the image type from a drop down menu, a clickable menu, byentering the type manually, or by any other means known in the art forselecting an option from a finite list or manually inputting data.Non-limiting examples of the image types include a CT image, an MRIimage, a PET image, a SPECT image, and a CTP image.

The method of FIG. 10 can also include an act 526 of selecting apresence or absence of injected radiocontrast in the one or morecross-sectional images. For example, a user may select one of the binaryoptions of injected radiocontrast for the one or more cross-sectionalimages. This act 526 may be performed by selecting a box answering aquestion as to whether the one or more images contain injectedradiocontrast, maybe a clickable selection, selected from a pull-downmenu, or by any other means known in the art for selecting an option.

The method of FIG. 10 can also include an act 528 of selecting one ormore target lesions. Act 528 can comprise identifying one or more targetlesions within the one or more cross-sectional images. For example, theuser may, through interactions with an interface as described above,select one or more target lesions using any communications device fortransmitting the user-identification to computing system 100, someexamples of which include a mouse, keyboard, touchscreen, or other meansof identifying and/or selecting information on a user interface that areknown in the art. In some embodiments, selecting the one or more targetlesions may comprise the user tracing the target lesion, using asmart-selection tool that, upon selecting a portion of the targetlesion, selects an area having similar pixel contrast, or other means ofselecting as known in the art.

The method of FIG. 10 can also include an act 530 of labeling a firsttarget lesion with a lesion type and an anatomical location. Forexample, the user may label a first target lesion with a lesion type(e.g., primary tumor, metastasis, or lymph node) by clickably selectingthe lesion type, wherein in some embodiments, the clickable selection islocated on a side bar of the user interface and comprises a primarytumor, metastasis, or lymph node; selecting the lesion type from a listor pull-down menu, manually entering the lesion type, or other meansknown in the art for labeling a digital image or digital medical imagedata. The user may similarly label a first target lesion with ananatomical location. In some embodiments, the anatomical locations arepre-set to encourage consistency of labeling. For example, the user willselect one of the pre-set labels, including, for example, “lung” so asto avoid potential user labels for the same anatomical location such as,for example, “lung_targetlesion1,” “right lung,” “lung1,” etc.

The method of FIG. 10 can also include an act 532 of segmenting thefirst target lesion into a region of interest. Act 532 may be repeatediteratively if there are more than one regions of interest. For example,the user may segment the first target lesion by using a segmenting toolprovided at the interface, may manually trace the regions of interest,or may segment the target lesion using any other means of segmenting orselecting features of objects, or objects themselves, within a digitalimage, including within digital medical image data.

The method of FIG. 10 can also include an act 534 of in response tocomputer input, modifying or deleting the region of interest. Forexample, the computing system may be using one or more tumor responsecriteria which require regions of interest to be a certain size, and ifcomputer may provide input to the user, such as, “the region of interestis too small” wherein the user modifies the region of interest, ifpossible, to accommodate the computer input. If the region of interestcannot be modified sufficiently by the user or the input is that toomany regions of interest have been selected, the user may delete and/orremove the region of interest. In some embodiments, deleting the regionof interest acts to remove the user placed selection without having aneffect on the underlying digital image.

The method of FIG. 10 can also include an act 536 of selecting one ormore additional target lesions until a total number of lesions areselected in accordance with a default or selected tumor responsecriteria. For example, the user may repeat method acts 528, 530, 532,and 534 until the total number of lesions are selected in accordancewith a default or selected tumor response criteria. Additionally oralternatively, the user may be directed to act 536 if the selected tumorresponse criteria are changed by the user or by the computing system.

The method of FIG. 10 can also include an act 538 of indicating aresponse of non-target lesions and a presence or absence of newmetastases. For example, the user may indicate a response by selectingthe non-target response from a pull-down menu, by entering the responsemanually, or by any other means known in the art for selecting aresponse. The user may also select the presence or absence of newmetastases by clickably selecting “Yes” or “No” in response to theprompt: “New Metastasis,” or similar. Additionally or alternatively, theuser may select the presence or absence of new metastases by using apull-down menu, entering the response manually, or by any other meansknown in the art.

The method of FIG. 10 can also include an act 540 of reviewing a summarydisplay of an objective tumor response and one or more lesion metricsfor the identified first target lesion. For example, the user may,directly or indirectly review a summary display, example outputs of suchprovided in FIGS. 2-5. The user may optionally return to any of themethod acts in FIG. 10, make one or more changes, and return to methodact 540, wherein the summary display may be updated with the one or morechanges.

Example 1

The following Table 1 lists abbreviations/acronyms used in any of Tables2-4 together with their meaning.

TABLE 1 List of Acronyms/Abbreviations Acronym/Abbreviation MeaningRECIST Response Evaluation Criteria in Solid Tumors PFS Progression-FreeSurvival OS Overall Survival CI Confidence Interval PR Partial ResponseSD Stable Disease PD Progressive Disease HR Hazard Ratio FR FavorableResponse IR Intermediate Response UR Unfavorable Response

Tables 2-4 include experimental and/or clinical data describedimmediately below and within FIG. 11. Particularly, the disclosed hazardratio (HR) is the ratio of the hazard rates corresponding to theresponder and nonresponder groups. The hazard rates indicate thelikelihood of progression-free survival (PFS) of the entire group. TheVTB Criteria has a HR of 5.7, indicating that the nonresponders were 5.7times more likely to progress than the responders. In Table 2, thep-values are calculated with respect to the indicated comparison.

In patients with metastatic clear-cell renal cell carcinoma, VTBCriteria nonresponders (N=120 patients with <30% decrease or an increasein the VTB or new metastases) on the initial post-therapy CT study were5.7 times more likely to progress (HR=5.70, 95% CI=4.07−7.97, p<0.001)than responders (N=155 patients with >30% decrease in the VTB). Theaverage percent decrease in VTB of 33.8% following one round of AAGtherapy in the full patient cohort (N=275) was significantly greater(p<0.001) than the average percent decrease in tumor length of 9.5%.While percent change in length was highly correlated with percent changein area (r=0.939), neither were as strongly correlated with percentchange in VTB (r=0.713 and 0.764, respectively), indicating that percentchange in VTB is a unique size metric. In a patient level analysis,inter-observer agreement was very good for assessing percent change inlength, area, and VTB (ICC=0.82, 0.89, and 0.88, respectively) but poorfor assessing percent change in mean attenuation (ICC=0.31), which isused in Choi Criteria and Modified Choi Criteria. These findingsindicated that quantitative changes in the VTB using thecomputer-assisted tumor response assessment as disclosed herein arestrongly predictive of patient survival, highly predictive of tumorresponse to AAG therapy, and highly reproducible.

TABLE 2 Data associated with each sextant of FIG. 11 Sample Median Size(N) PFS 95% CI RECIST PR 20 1.39 0.89-∞  SD 236 1.13 0.91-1.40 PD 190.19 0.09-0.32 10% Tumor Shrinkage PR 138 1.65 1.38-1.91 SD 118 0.680.54-0.88 PD 19 0.19 0.09-0.32 Choi PR 207 1.27 0.98-1.58 SD 45 0.850.43-1.12 PD 23 0.21 0.09-0.63 modified Choi PR 118 1.65 1.36-1.94 SD126 0.88 0.68-1.05 PD 31 0.21 0.19-0.42 MASS FR 135 1.24 0.92-1.60 IR121 1.05 0.71-1.38 UR 19 0.19 0.09-0.32 VTB PR 155 1.79 1.41-1.98 SD 1010.54 0.44-0.68 PD 19 0.19 0.09-0.33

TABLE 3 Additional Data associated with each sextant of FIG. 11Responders Nonresponders HR (N) (N) 95% CI p-value RECIST 1.54 255 200.85-2.77 0.148 10% Tumor 2.98 137 13 2.19-4.05 <0.001 Shrinkage Choi2.52 68 207 1.79-3.55 <0.001 modified 2.32 157 118 0.85-2.77 <0.001 ChoiMASS 1.76 140 135 1.31-2.37 <0.001 VTB 5.70 120 155 4.07-7.97 <0.001

TABLE 4 Hazard ratios between response categories of each imagingcriteria PFS OS HR 95% CI p-value HR 95% CI p-value RECIST PR vs SD 0.700.39-1.27 0.241 0.27 0.11-0.67 0.005 PD vs SD 10.63  6.08-18.57 <0.0014.22 2.53-7.04 <0.001 10% Tumor PR vs SD 0.38 0.27-0.52 <0.001 0.370.27-0.52 <0.001 Shrinkage PD vs SD 6.89  3.91-12.14 <0.001 2.771.64-4.68 <0.001 Choi PR vs SD 0.53 0.35-0.79 0.002 0.55 0.37-0.80 0.002PD vs SD 3.94 2.14-7.25 <0.001 1.52 0.86-2.69 0.155 Modified Choi PR vsSD 0.51 0.37-0.71 <0.001 0.42 0.29-0.59 <0.001 PD vs SD 5.47 3.36-8.92<0.001 1.92 1.22-3.02 0.005 MASS FR vs IR 0.66 0.48-0.90 0.009 0.640.46-0.90 0.009 UR vs IR 8.84  4.97-15.69 <0.001 3.65 2.14-6.21 <0.001VBT PR vs SD 0.19 0.13-0.27 <0.001 0.33 0.24-0.46 <0.001 PD vs SD 3.772.07-6.86 <0.001 3.24 1.91-5.47 <0.001

Example 2

In a large inter-observer analysis study with 11 different readers from10 different institutions assessing cross-sectional images from 20patients with metastatic renal cell carcinoma treated with AAG therapy,mean tumor assessment time with computer-assisted tumor responseassessment, as disclosed herein, and measurement of the VTB was 50%faster than a routine image viewer and manual data entry (7 vs. 14 min,p<0.001), and patient-level errors were significantly less common (0%vs. 31%, p<0.001). Using the computer-assisted tumor response assessmentas disclosed herein, inter-observer agreement was very good formeasuring the VTB vs. good for measuring tumor length (intra-classcorrelation coefficient=0.96 vs. 0.68, p=0.021). These findings suggestthat tumor response assessment using the computer-assisted tumorresponse assessment disclosed herein and measurement of the vasculartumor burden is significantly faster, associated with a marked reductionin errors, and associated with substantial inter-observer agreementcompared to manual methods that comprise the current standard of care.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above,or the order of the acts described above. Rather, the described featuresand acts are disclosed as example forms of implementing the claims.

Embodiments of the present invention may comprise or utilize aspecial-purpose or general-purpose computer system that includescomputer hardware, such as, for example, one or more processors andsystem memory, as discussed in greater detail below. Embodiments withinthe scope of the present invention also include physical and othercomputer-readable media for carrying or storing computer-executableinstructions and/or data structures. Such computer-readable media can beany available media that can be accessed by a general-purpose orspecial-purpose computer system. Computer-readable media that storecomputer-executable instructions and/or data structures are computerreadable hardware storage devices. Computer-readable media that carrycomputer-executable instructions and/or data structures are transmissionmedia. Thus, by way of example, and not limitation, embodiments of theinvention can comprise at least two distinctly different kinds ofcomputer-readable media: computer readable hardware storage devices andtransmission media.

Computer readable hardware storage devices are physical storage mediathat store computer-executable instructions and/or data structures.Physical storage media include computer hardware, such as RAM, ROM,EEPROM, solid state drives (“SSDs”), flash memory, phase-change memory(“PCM”), optical disk storage, magnetic disk storage or other magneticstorage devices, or any other hardware storage device(s) which can beused to store program code in the form of computer-executableinstructions or data structures, which can be accessed and executed by ageneral-purpose or special-purpose computer system to implement thedisclosed functionality of the invention.

Transmission media can include a network and/or data links which can beused to carry program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system. A “network” isdefined as one or more data links that enable the transport ofelectronic data between computer systems and/or modules and/or otherelectronic devices. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computersystem, the computer system may view the connection as transmissionmedia. Combinations of the above should also be included within thescope of computer-readable media.

Further, upon reaching various computer system components, program codein the form of computer-executable instructions or data structures canbe transferred automatically from transmission media to computerreadable hardware storage devices (or vice versa). For example,computer-executable instructions or data structures received over anetwork or data link can be buffered in RAM within a network interfacemodule (e.g., a “NIC”), and then eventually transferred to computersystem RAM and/or to less volatile computer readable hardware storagedevices at a computer system. Thus, it should be understood thatcomputer readable hardware storage devices can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at one or more processors, cause ageneral-purpose computer system, special-purpose computer system, orspecial-purpose processing device to perform a certain function or groupof functions. Computer-executable instructions may be, for example,binaries, intermediate format instructions such as assembly language, oreven source code.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The inventionmay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. As such, ina distributed system environment, a computer system may include aplurality of constituent computer systems. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Those skilled in the art will also appreciate that the invention may bepracticed in a cloud-computing environment. Cloud computing environmentsmay be distributed, although this is not required. When distributed,cloud computing environments may be distributed internationally withinan organization and/or have components possessed across multipleorganizations. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services). The definition of “cloudcomputing” is not limited to any of the other numerous advantages thatcan be obtained from such a model when properly deployed.

A cloud-computing model can be composed of various characteristics, suchas on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, and so forth. A cloud-computing model mayalso come in the form of various service models such as, for example,Software as a Service (“SaaS”), Platform as a Service (“PaaS”), andInfrastructure as a Service (“IaaS”). The cloud-computing model may alsobe deployed using different deployment models such as private cloud,community cloud, public cloud, hybrid cloud, and so forth.

Some embodiments, such as a cloud-computing environment, may comprise asystem that includes one or more hosts that are each capable of runningone or more virtual machines. During operation, virtual machines emulatean operational computing system, supporting an operating system andperhaps one or more other applications as well. In some embodiments,each host includes a hypervisor that emulates virtual resources for thevirtual machines using physical resources that are abstracted from viewof the virtual machines. The hypervisor also provides proper isolationbetween the virtual machines. Thus, from the perspective of any givenvirtual machine, the hypervisor provides the illusion that the virtualmachine is interfacing with a physical resource, even though the virtualmachine only interfaces with the appearance (e.g., a virtual resource)of a physical resource. Examples of physical resources includingprocessing capacity, memory, disk space, network bandwidth, mediadrives, and so forth.

In addition, as used in the specification and appended claims,directional terms, such as “top,” “bottom,” “up,” “down,” “upper,”“lower,” “proximal,” “distal,” “horizontal,” “vertical,” “adjacent,” andthe like are used herein solely to indicate relative directions and arenot otherwise intended to limit the scope of the invention or claims.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A computer system for determining and evaluatingan objective tumor response to an anti-cancer therapy usingcross-sectional images, the computer system comprising: one or moreprocessors; and one or more computer readable hardware storage devices,wherein the one or more computer readable hardware storage devicescomprise computer-executable instructions that when executed by at leastone of the one or more processors cause the computer system to performat least the following: receive user data identifying one or more targetlesions from one or more cross-sectional images of digital medical imagedata; for each of the one or more target lesions identified: receivingadditional user data identifying a target lesion type, wherein thetarget lesion type comprises a mass or a lymph node; receivingadditional user data identifying a target lesion anatomical location;activating a segmenting tool for segmenting the one or more targetlesions into one or more regions of interest; automatically extractingone or more lesion metrics from the one or more segmented regions ofinterest, wherein the one or more lesion metrics are automaticallyextracted in accordance with a user-selected or predefined tumorresponse criterion; and prompting the user to modify a user-segmentedregion of interest or to delete an associated target lesion that doesnot conform to one or more rules associated with the user-selected orpredefined tumor response criterion; repeat receiving user dataidentifying the one or more target lesions until a total number oflesions is selected in accordance with the user-selected or predefinedtumor response criterion; receive a response of one or more non-targetlesions to the anti-cancer therapy based on the user-selected orpredefined tumor response criterion; receive a presence or an absence ofone or more new lesions; determine one or more changes to the one ormore lesion metrics; and derive an objective tumor response based on theuser-selected or predefined tumor response criterion.
 2. The computersystem as in claim 1, wherein the computer-executable instructionsfurther include computer-executable instructions that when executed bythe at least one processor cause the computer system to receive an imagetype for the one or more cross-sectional images; and receive adetermination of injected radiocontrast in the one or morecross-sectional images.
 3. The computer system as in claim 2, whereinthe image type and the determination of injected radiocontrast arereceived from one or more metadata tags associated with the one or morecross-sectional images.
 4. The computer system as in claim 2, whereinthe image type and the determination of injected radiocontrast arereceived from a user selection.
 5. The computer system as in claim 1,wherein segmenting the one or more target lesions is performed with afree-form region of interest tool.
 6. The computer system as in claim 1,wherein the user-selected or predefined tumor response criterioncomprise a default or a user-specified tumor response criteria selectedfrom the group consisting of: Response Evaluation Criteria in SolidTumors (RECIST) 1.0, RECIST 1.1, modified RECIST, World HealthOrganization (WHO) Criteria, 10% Tumor Diameter Shrinkage Criteria, ChoiCriteria, Modified Choi Criteria, Morphology Attenuation Size andStructure (MASS) Criteria, Immune-related Response Criteria, ChesonCriteria, lymphoma response criteria, Revised Response Criteria forMalignant Lymphoma, Positron Emission Tomography Response Criteria inSolid Tumors (PERCIST), Metabolic Response Criteria, EuropeanOrganization for Research and Treatment of Cancer (EORTC), Internationaluniform response criteria for multiple myeloma, Current ResponseCriteria for High-Grade Gliomas, MacDonald Criteria, Response Assessmentof Neuro-Oncology (RANO) Criteria, Vascular Tumor Burden (VTB) Criteria,and computed tomography texture analysis criteria.
 7. The computersystem as in claim 1, wherein the objective tumor response is derivedfrom at least the user-selected or predefined tumor response criterionand the one or more lesion metrics.
 8. The computer system as in claim1, wherein the one or more lesion metrics comprise one or more of alongest dimension length; a short axis dimension length; a longestdimension length of vascularized tumor; a pixel area of the at least oneof the one or more target lesions; a volume of the at least one of theone or more target lesions; a maximum standardized uptake value of theone or more target lesions; a metabolic tumor volume of the one or moretarget lesions; a total lesion glycolysis of the one or more targetlesions; a pixel area within the first restricted range; a pixel areawithin the second restricted range; a mean value of pixel intensitieswithin the total range of pixel intensities; a mean value of pixelintensities within the first restricted range of pixel intensities; amedian value of the pixel intensities within the total range of pixelintensities; a maximum value of the pixel intensities within the totalrange of pixel intensities; a histogram parameter, wherein the histogramparameter comprises a quantitative distribution of pixel intensities inthe at least one of the one or more target lesions; or a textureparameter, wherein the texture parameter comprises a geographicdistribution of pixel intensities in the at least one of the one or moretarget lesions.
 9. The computer system as in claim 1, wherein thecomputer-executable instructions further include computer-executableinstructions that when executed by the at least one processor cause thecomputer system to generate a summary display comprising the objectivetumor response and the one or more lesion metrics.
 10. The computersystem as in claim 9, wherein the summary display additionally comprisesone or more of: a first illustration, wherein the first illustrationcomprises an illustration of the at least one of the one or more targetlesions at the first point in time; a second illustration, wherein thesecond illustration comprises an illustration of the at least one of theone or more target lesions at the second point in time; and at least oneadditional component selected from the group consisting of: a thirdillustration, wherein the third illustration comprises an illustrationof the vascular tumor burden at the first point in time; a fourthillustration, wherein the fourth illustration comprises an illustrationof the vascular tumor burden at the second point in time; a fifthillustration, wherein the fifth illustration comprises an illustrationof the necrotic tumor burden at the first point in time; a sixthillustration, wherein the sixth illustration comprises an illustrationof the necrotic tumor burden at the second point in time; a seventhillustration, wherein the seventh illustration comprises an illustrationof a total tumor burden at the first point in time; an eighthillustration, wherein the eighth illustration comprises an illustrationof the total tumor burden at the second point in time; a first graphicaldisplay illustrating one or more changes in vascularized tumor betweenthe first point in time and the second point in time; a second graphicaldisplay illustrating one or more changes in necrotic tumor between thefirst point in time and the second point in time; a numeric valuerepresenting at least one of a percent change, an average change, or anabsolute change in the one or more lesion metrics; a first indication ofobjective response, wherein the first indication comprises an indicationthat the at least one of the one or more target lesions is responding ornot responding to anti-cancer therapy; a second indication of objectiveresponse, wherein the second indication of objective response comprisesan indication that the one or more non-target lesions is responding ornot responding to anti-cancer therapy; a first readable text, whereinthe first readable text comprises the one or more lesion metrics; asecond readable text, wherein the second readable text comprises thepresence or absence of new lesions; a third readable text, wherein thethird readable text comprises the vascular tumor burden for the at leastone of the one or more target lesions; a fourth readable text, whereinthe fourth readable text comprises the necrotic tumor burden for the atleast one of the one or more target lesions; or a fifth readable text,wherein the fifth readable text comprises the objective tumor responsefor the one or more target lesions as determined by the user-selected orpredefined tumor response criterion.
 11. A computer-implemented methodfor determining and evaluating an objective tumor response to ananti-cancer therapy using cross-sectional images, the method comprising:receiving user data identifying one or more target lesions from one ormore cross-sectional images of digital medical image data; identifyingone or more rules associated with a user-selected or predefined tumorresponse criterion; activating a segmenting tool for segmenting the oneor more target lesions into one or more regions of interest; promptingthe user to modify a user-specified region of interest or to delete anassociated target lesion from the identified cross-sectional images thatdoes not conform to the one or more rules associated with theuser-selected or predefined tumor response criterion; automaticallyextracting one or more lesion metrics from the one or more segmentedregions of interest, wherein the one or more lesion metrics areautomatically extracted in accordance with the user-selected orpredefined tumor response criterion; receiving a presence or an absenceof one or more new lesions; and generating a summary display comprisingthe user selected or predefined tumor response criterion and one or morelesion metrics.
 12. The method as in claim 11, further comprisingreceiving an image type for the one or more cross-sectional images andreceiving a determination of injected radiocontrast in the one or morecross-sectional images, wherein the image type and the determination ofinjected radiocontrast are received from one or more metadata tagsassociated with the one or more cross-sectional images or from a userselection.
 13. The method as in claim 11, wherein the user-selected orpredefined tumor response criterion comprise a default or auser-specified tumor response criteria selected from the groupconsisting of: Response Evaluation Criteria in Solid Tumors (RECIST)1.0, RECIST 1.1, modified RECIST, World Health Organization (WHO)Criteria, 10% Tumor Diameter Shrinkage Criteria, Choi Criteria, ModifiedChoi Criteria, Morphology Attenuation Size and Structure (MASS)Criteria, Immune-related Response Criteria, Cheson Criteria, lymphomaresponse criteria, Revised Response Criteria for Malignant Lymphoma,Positron Emission Tomography Response Criteria in Solid Tumors(PERCIST), Metabolic Response Criteria, European Organization forResearch and Treatment of Cancer (EORTC), International uniform responsecriteria for multiple myeloma, Current Response Criteria for High-GradeGliomas, MacDonald Criteria, Response Assessment of Neuro-Oncology(RANO) Criteria, Vascular Tumor Burden (VTB) Criteria, and computedtomography texture analysis criteria.
 14. The method as in claim 11,further comprising: repeating the identification of the one or moretarget lesions until a total number of lesions are selected inaccordance with the user-selected or predefined tumor responsecriterion; receiving a response of one or more non-target lesions to theanti-cancer therapy based on the user-selected or predefined tumorresponse criterion; determining one or more changes to the one or morelesion metrics; and deriving an objective tumor response based on theuser-selected or predefined tumor response criterion.
 15. A computerprogram product comprising one or more hardware storage devices havingstored thereon computer-executable instructions that are executable byone or more processors of a computer system and that configure thecomputer system to determine and evaluate an objective tumor response toan anti-cancer therapy using one or more cross-sectional images,including computer-executable instructions that configure the computersystem to perform at least the following: (i) identify one or more rulesassociated with a user-selected or predefined tumor response criterion;(ii) identify a cross-sectional image of digital medical image datacomprising a user-selected target lesion; for the user-selected targetlesion, (iii) identify a target lesion type comprising a mass or a lymphnode; (iv) identify a target lesion anatomical location; and (v) prompta user to modify or delete a user-specified region of interest or theuser-selected target lesion if it does not meet one or more requirementsassociated with the user-selected or predefined tumor responsecriterion; (vi) repeat steps (i)(v) until a total number of targetlesions identified is in accordance with the user-selected or predefinedtumor response criterion; and (vii) generate a summary displaycomprising the user selected or predefined tumor response criterion andthe one or more lesion metrics.
 16. The computer program product as inclaim 15, wherein the computer-executable instructions additionallyconfigure the computer system to automatically extract one or morelesion metrics from the user-selected target lesion, wherein the one ormore lesion metrics are automatically extracted in accordance with theuser-selected or predefined tumor response criterion, and wherein theone or more lesion metrics comprise at least one of: a longest dimensionlength; a short axis dimension length; a longest dimension length ofvascularized tumor; a pixel area of the at least one of the one or moretarget lesions; a volume of the at least one of the one or more targetlesions; a maximum standardized uptake value of the one or more targetlesions; a metabolic tumor volume of the one or more target lesions; atotal lesion glycolysis of the one or more target lesions; a pixel areawithin the first restricted range; a pixel area within the secondrestricted range; a mean value of pixel intensities within the totalrange of pixel intensities; a mean value of pixel intensities within thefirst restricted range of pixel intensities; a median value of the pixelintensities within the total range of pixel intensities; a maximum valueof the pixel intensities within the total range of pixel intensities; ahistogram parameter, wherein the histogram parameter comprises aquantitative distribution of pixel intensities in the at least one ofthe one or more target lesions; or a texture parameter, wherein thetexture parameter comprises a geographic distribution of pixelintensities in the at least one of the one or more target lesions. 17.The computer program product as in claim 15, further comprisingcomputer-executable instructions that are executable to configure thecomputer system to perform at least the following: identify across-sectional image of digital medical image data captured at a secondpoint in time that comprises the user-selected target lesion at thesecond point in time; automatically determine one or more changes to theone or more lesion metrics at the second point in time; and derive anobjective tumor response based on the one or more changes to the one ormore lesion metrics and based on the user selected or predefined tumorresponse criterion.
 18. The computer program product as in claim 17,further comprising computer-executable instructions that are executableto configure the computer system to generate an updated summary displaycomprising the user selected or predefined tumor response criterion andthe objective tumor response.
 19. The computer program product as inclaim 15, further comprising computer-executable instructions that areexecutable to configure the computer system to prompt the user to modifyone or more lesion metrics in accordance with one or more requirementsof the user selected or predefined tumor response criterion.
 20. Thecomputer program product as in claim 19, wherein the one or more lesionmetrics comprise a longest dimension length or a short axis dimensionlength.